Pratham International

Location: Remote - India AI Engineer - ASR: PraDigi, a Pratham Initiative About Pratham International Pratham International is a US 501(c)(3) established to support learning innovations for children and youth in contexts across the globe. We enable the adaptation and spread of proven and scalable education solutions pioneered in India by Pratham Education Foundation to contexts across the global south through programming and technical assistance tailored to local realities.  Today, Pratham reaches approximately 7.5 million children annually across 23 Indian states and union territories and an additional 1 million across 31 countries.. Through the Pratham Shah PraDigi Innovation Centre (PraDigi), Pratham International also seeks to create technology innovations for low-resource communities, and support models aimed at learning for school, work, and life for children and youth. We work on several cutting-edge projects in the domain of Generative AI, Automatic Speech Recognition, and Natural Language Processing. The team’s innovative technology products include the Anytime Testing Model (ATM), an AI-enabled question generation and grading system that allows learners to be assessed on any topic, anytime; PadhAI: Reading Assessment App, which uses advanced speech recognition to evaluate children’s reading fluency in Hindi and eight Indian languages; and a suite of LLM-powered chatbots designed for diverse learning contexts: from BaalSakhi for early childhood care, to a skilling interview bot for youth employment readiness, and OCR-based analysis tools that digitize and assess student work at scale. At Pratham International, we build for the long term. Whether it’s ensuring products are truly accessible across rural India and the global south through languages, building datasets to ensure context and accuracy for products, or gathering feedback from the field.   Backed by partners like Google.org, Anthropic, and the Gates Foundation, we are committed to building digital public goods that ensure every child is in school and learning well. Description of the Role Pratham International, through its wholly-owned subsidiary in India (PULS Pvt. Ltd.), is looking to hire an AI Engineer for the PraDigi Initiative. As an ASR Engineer, you will lead the development and deployment of speech recognition systems for automated reading assessment across multiple languages.  The engineer will work across the full ASR pipeline: from supervising data collection, annotation, data preparation and model training to decoding, alignment, error analysis, and deployment. This is a hands-on role at the intersection of speech recognition, multilingual NLP, and educational assessment. Key responsibilities include: Data Collection and Annotation Provide technical inputs to speech data collection design, including sampling across languages, dialects, age groups, reading levels, recording environments, devices, and target items to ensure the dataset is representative and useful for model training and evaluation. Define annotation guidelines and quality checks for transcripts, reading errors, disfluencies,  background speech and other phenomena relevant to reading assessment. Work with data and field teams to identify data gaps and annotation inconsistencies through model error analysis, and recommend targeted additional data collection or re-annotation where needed. Help design annotation schemas and inter-annotator quality processes so labels are consistent enough for model training, alignment, and evaluation. Model Training Train, fine-tune, and evaluate ASR models for children’s read speech across multiple languages and accents. Build pipelines for audio preprocessing, segmentation, normalization, transcription alignment, and model training. Adapt existing ASR architectures such as Whisper, wav2vec 2.0, Conformer, CTC/RNN-T, or similar models for reading assessment. Optimize models for practical deployment environments, including low-bandwidth, store-and-forward, server-based, and potentially offline/mobile inference. Adopting for Reading Assessment Develop approaches for accurately detecting substitutions, omissions, insertions, repetitions, partial-word attempts, hesitations and self-corrections. Implement forced alignment, phoneme-level alignment, or pronunciation-scoring approaches where required. Conduct systematic error analysis by language, reading level, age, accent, gender, recording conditions, and device. Collaborate with assessment experts and field teams to translate model outputs into measures such as reading accuracy, WCPM, fluency, and reading level. Benchmarking Define and track ASR metrics relevant to reading assessment, including WER/CER, word-level accuracy, error-detection precision/recall, alignment accuracy, and reading-score agreement with human evaluators. Build reproducible experimentation and model-evaluation pipelines and maintain clear technical documentation. Desired Experience and Qualification We are looking for an AI/ML Engineer with ASR expertise that can own the ASR based reading assessment pipeline across multiple languages A Degree in any Mathematics/Computer Science/Data Science or related STEM field 3-5 years of full-time experience; at least 2 working in an AI/ML role Strong experience in automatic speech recognition, speech processing, or spoken-language technologies. Strong Python skills and experience with PyTorch or similar deep-learning frameworks. Experience working with modern ASR architectures and toolkits such as Hugging Face Transformers, NeMo, Kaldi, or equivalent. Understanding of ASR concepts including: -acoustic modelling -CTC / transducer / encoder-decoder architectures -decoding and beam search -language models -forced alignment -phoneme representations -WER/CER and ASR error analysis Experience preparing and working with large-scale audio and transcription datasets. Ability to diagnose model errors systematically rather than relying only on aggregate transcription accuracy. Why join us? Impact at a Global Scale: Your work will directly support the learning journeys of millions of children. With programs active in 31 countries, you will help build Digital Public Goods that bridge the gap for the millions of children across the Global South who still struggle with foundational literacy. Unique Technical Challenges : We apply advanced machine learning where it is needed most. You will solve "last-mile" problems—such as training ASR models on children's voices and optimizing LLMs for offline, low-bandwidth environments—that are rarely addressed in the commercial sector. A Culture of Growth and Evidence : At Pratham, we value rigorous testing and continuous learning. You will have the autonomy to experiment with the latest SOTA models, supported by a 5-year strategic roadmap and funding from partners like the Gates Foundation and Anthropic. High-Calibre, Mission-Driven Team : You will work alongside a global team of experts from institutions like MIT, Harvard, and INSEAD. Our alumni have gone on to lead successful startups and international policy shifts, all while remaining dedicated to the goal of "Every Child in School and Learning Well." Inclusive and Collaborative Environment : Join a diverse team that spans Asia, Africa, and Latin America. We value innovative thinking and local expertise, ensuring that the technology we build is deeply rooted in the realities of the communities we serve. Employment Details Contract Type : Full-Time Employment Date of Joining : Immediate Work from Anywhere in India : We offer a flexible, remote-first work environment for candidates based anywhere in India. While you have the autonomy to work from where you are most productive, you will have the unique opportunity to travel to our field locations periodically. These visits are essential to see your models in the hands of learners and to gather the real-world feedback that drives our innovation. Competitive Remuneration : Salary is benchmarked against industry standards for AI roles and is commensurate with experience. As a global organization, we are committed to providing a package that recognizes the high-market value of your technical expertise alongside the deep social impact of your work. To apply Fill out the form attached Shortlisted candidates will be contacted for further rounds Please refer to this Guidance on Candidates AI Usage by Anthropic to understand how to present yourself to Pratham International effectively Pratham International is an Equal Opportunity Employer We are dedicated to creating a diverse and inclusive workplace. We welcome applications from all qualified individuals, regardless of race, gender, disability, or any other legally protected status.

Location: Remote - India AI/ML Solution Architect Education & Assessment Platforms About Pratham International Pratham International is a US 501(c)(3) established to support learning innovations for children and youth in contexts across the globe. We enable the adaptation and spread of proven and scalable education solutions pioneered in India by Pratham Education Foundation to contexts across the global south through programming and technical assistance tailored to local realities. Today, Pratham innovations reach millions of children annually through partnerships across ~30 countries. Through the Pratham Shah PraDigi Innovation Centre (PraDigi), Pratham International also seeks to create technology innovations for low-resource communities, and support models aimed at learning for school, work, and life for children and youth. We work on several cutting-edge projects in the domain of Generative AI, Automatic Speech Recognition, and Natural Language Processing. The team’s innovative technology products include the PadhAI: Reading Assessment App, which uses advanced speech recognition to evaluate children’s reading fluency in 8 Indian languages and 6 global languages; Anytime Testing Machine (ATM), an AI-enabled question generation and grading system that allows learners to be assessed on any topic, anytime; and a suite of AI-powered chatbots designed for diverse learning contexts: from early childhood care, to youth employment readiness, and OCR-based analysis tools that digitize and assess student work at scale. At Pratham International, we build for the long term. Whether it’s ensuring products are truly accessible across rural India and the global south through languages, building datasets to ensure context and accuracy for products, or gathering feedback from the field. Backed by partners like Google.org, Anthropic, and the Gates Foundation, we are committed to building digital public goods that ensure every child is in school and learning well. Role Overview About the work We design and deliver AI systems that governments and large education programmes can actually run: curriculum-aligned item generation, paper assembly, handwriting OCR, rubric grading, and teacher-in-the-loop review. The work is already live as practice assessment in India and is being built as a national exam platform in Rwanda, with further country programmes behind it. We are hiring a Solution Architect who sits between discovery and delivery. You will turn messy workshop notes and ministry requirements into architecture a team can build, secure, cost, and operate — RAG and multi-agent systems, LLM-as-judge pipelines, and the cloud/LLMOps layer underneath. You will Own solution design from first workshop to a delivery-ready blueprint (services, data stores, trust boundaries, cost, ops). Architect LLM pipelines: generation, retrieval over curriculum knowledge graphs, standards/duplication/quality judges, constraint-based paper assembly. Design human + AI workflows with audit, segregation of duties, cycle-pinned config, and no student PII in model calls. Choose and govern models, prompts, eval gates, and fallbacks (including OCR: Azure primary, secondary fallback). Shape the Azure/AWS platform: identity, networking, LLMOps/MLOps, observability, and sovereign-hosting constraints. Run client workshops, write POVs and architecture packs, and stay accountable until the system is in production. About the work Pratham International programmes sit at the intersection of pedagogy, public systems, and production AI. The pattern is consistent across countries; the constraints change. Anytime Testing Machine (ATM) — India, practice and feedback End-to-end assessment for real classroom conditions: generate curriculum-aligned items, digitise handwritten answers (often photographed on a phone), grade against rubrics, and return feedback a teacher reviews before it reaches the learner. Built for high pupil–teacher ratios, mixed language (e.g. Hindi + English), and uneven connectivity. Pilots have already reached thousands of learners, including Second Chance (women returning for Grade 10). Model quality is measured against expert golden sets, not vendor slides. National assessment automation A government-grade exam platform. Phase 1 is item authoring and print-ready paper composition before the exam window. Phase 2 is script intake, OCR at national volume, AI-assisted grading with human confirmation on the pilot, results export, and monitoring/evaluation reporting. Subjects span core and elective streams across the curriculum. Design rules that do not exist in a typical SaaS product: cycle-pinned configuration, embargoed papers, hash-chained audit trails, and integration with national student information and assessment management systems. Curriculum knowledge graphs Structured retrieval corpora (concepts + worked examples) that ground generation and judging. This is the standing public-knowledge exception in an otherwise closed exam system, and the seed of a broader education DPI conversation. AI in TaRL and teacher support AI tools that help teachers group and teach by learning level, with an RCT-shaped evidence bar. Same product family: human agency stays in the loop; the model is a capacity multiplier. Country programmes and the three-year direction Adapt the same engine to new ministries, languages, and exam rules (Kenya and other Global South programmes are in scope). The longer bet is a learning and credentialing engine that recognises competence beyond a single syllabus. This is not a chatbot brief. It is constrained generation, retrieval, judging, document AI, workflow, and public-sector operations. Why this role exists Most enterprise AI dies between the workshop and production. We need one person who owns that gap: someone who can sit with NESA, C4IR, Pratham pedagogy, and engineering in the same week, and leave behind a blueprint the delivery team can execute without inventing policy. The target profile is a practising AI/ML Solution Architect — not a pre-sales slide owner, and not an infra-only platform engineer. The person should have done all three of these: Designed GenAI / NLP solutions, PoCs, and client workshops, and can talk to research-grade issues (eval, agent safety, prompt-injection, model-specific behaviour). Owned the AI platform: LLMOps, MLOps, DevSecOps, SRE habits, cloud migration, and FinOps. Taken messy discovery and turned it into Azure-centred RAG and multi-agent designs that a security team will accept. Key Responsibilities Discovery → architecture Lead solution workshops with programme, ministry, and engineering stakeholders. Convert half-formed requirements into service cuts, data stores, sequence diagrams, decision gates, and open-question lists with owners. Write architecture decision records. When the docs disagree (they will), force a decision instead of coding both. AI system design Design LLM jobs as contracts: inputs, JSON schema, temperature, persist prompt hash / model / tokens / latency. Architect generation grounded in structured curriculum blueprints and knowledge-graph retrieval, not "ask the model for a maths paper." Design LLM-as-judge banks (cognitive-level, relevance, difficulty, language, fairness, standards, duplication, blueprint compliance) with thresholds, one-repair rules, and golden-dataset release gates. Design hybrid assembly: constraint solver first, model only on ties and layout exceptions. Design document AI: primary OCR provider, secondary fallback provider, confidence routing, de-identification before any grading call. Specify eval: inter-rater agreement, rubric-band exact match, first-pass item acceptance, cost per paper cycle. Platform, trust, cost Own the reference architecture on the chosen cloud platform(s): identity, RBAC + segregation of duties, private networking, key management, object storage for scans, vector index, data warehouse for monitoring/evaluation reporting. Draw trust boundaries. Student PII never crosses the model gateway. Prompts are curriculum + knowledge-graph + blueprint content only. Put FinOps on the design: token budgets, OCR cost caps, degradation behaviour when a cap is hit, model routing. Specify LLMOps/MLOps: prompt/version registry, eval regression as a release gate, observability, rollback. Design for sovereign hosting and exam-cycle operations (embargo, watermarking, append-only audit, cycle-pinned configuration). Delivery and thought leadership Stay with the build through the first production cycle. Architecture that cannot be implemented is unfinished work. Produce POVs, sequence packs, and workshop artefacts that can be reused in the next country. Mentor tech leads on where the model should stop and the workflow should start. Success in the first two quarters First 30 days — Orient Complete architecture walkthroughs of every live/in-flight system with the current engineering leads; document current state (services, data stores, trust boundaries) as-is, not as-designed Meet every active stakeholder group once: engineering, programme/domain leads, and any external partner or government-side counterpart Inventory all open technical decisions and disagreements in existing specs; log each with current owner (if any) and status Identify the single highest-risk unresolved design question and flag it upward Day 60 — Diagnose Produce a current-state architecture diagram covering every major pipeline: data flow, model/LLM touchpoints, storage, and identity/access boundaries Assign an owner and target resolution date to every open contradiction identified in month 1 Draft a v1 reference architecture for one representative end-to-end workflow: service breakdown, data stores, identity/access model, model gateway placement, and a first-pass cost estimate Day 90 — Commit v1 reference architecture is reviewed and accepted by engineering and relevant stakeholders (not just self-signed) Can walk any core pipeline end-to-end from memory and name every data store, every quality/eval gate, and every trust boundary in it At least one architecture decision record is published for a previously-contested design question, with the decision forced rather than left open A first cost model exists for the reference architecture, with at least one identified lever for reducing spend if usage scales Day 120 — Harden Core pipeline components (generation, retrieval, judging/eval, or equivalent stages for this domain) have documented contracts: defined inputs/outputs, schema, and logged metadata (model, version, cost, latency) An evaluation harness exists for at least one component, with a defined pass/fail threshold A release gate exists that can block a bad prompt, model, or config change from reaching production Day 150 — Extend The next major system phase or capability is specified to the same rigor as the first: trust boundaries, failure modes, fallback behavior, and cost caps documented before build starts FinOps guardrails are in place for at least one production workload: budget ceilings, degradation behavior when a cap is hit, and a routing or fallback strategy Day 180 — Generalize A reusable design pattern or adaptation pack exists, separating what is fixed in the architecture from what varies by deployment context (e.g. language, hosting environment, external-system integration, content/domain rules) At least one engineering or technical lead has been mentored on where model-based logic should stop and deterministic/workflow logic should take over A second production-readiness review (security, cost, or operations) has been passed on a system this person designed or substantially redesigned Qualifications and Experience Must have 8+ years building software or data/AI systems, with recent experience as a Solution Architect, AI/ML Architect, or Principal/Staff Engineer with end-to-end architecture ownership. Shipped at least one production AI/ML system — RAG, multi-agent, LLM-as-judge, or a comparable classical ML/NLP pipeline at scale — that passed a real security, cost, and operations review, not just a pilot or a slide deck. Can produce a delivery-ready architecture blueprint: services, data stores, IAM, failure modes, evaluation strategy, and cost model — detailed enough that engineering isn't guessing at intent. Deep cloud experience on Azure and/or AWS: comfortable defending a landing-zone design, private-endpoint architecture, and a real monthly compute/inference/OCR spend. Fluent in Python, with hands-on experience in both classical ML (model training, evaluation, feature pipelines) and how LLM APIs, embeddings, and vector search actually behave under production load. Comfortable running a room with non-engineers — domain specialists, IT stakeholders, monitoring/evaluation teams — and translating fluently in both directions. Strong written English; this role's output is documentation that functions as a binding technical contract. Strongly preferred Azure AI (AI Foundry / OpenAI / Document Intelligence), plus one of AWS (Bedrock, SageMaker) or GCP (Vertex, BigQuery). LLMOps / MLOps: prompt + model versioning, MLflow or equivalent, eval harnesses, CI that can block a bad prompt. Document AI / OCR at volume; handwriting and low-quality scans, not only clean PDFs. RAG over structured knowledge (graphs, ToS, textbooks) and hybrid search, not only naive chunking. Education, assessment, public sector, or other high-audit domains (health, finance, identity). Certifications: Azure Data Scientist / Azure Solutions Architect; AWS SAA, ML Specialty, or Data Analytics Specialty. Experience with agent security: tool-use limits, DLP on egress, prompt-injection evaluation that is model-specific. FinOps discipline. You have killed a design because it would not survive the token bill. Nice to have Microsoft Fabric, Databricks, Semantic Kernel / Microsoft Agent Framework, MCP (Model Context Protocol) tool integration for agentic workflows. Constraint solvers or exam-blueprint / item-bank systems for automated test assembly. Multilingual generation and feedback, including code-mixed language pairs (e.g. Hindi–English, Spanish–English, or similar). Experience standing up golden/eval datasets with subject-matter experts. Prior work with government data systems, data-sharing agreements, or in-country/sovereign hosting requirements. What this role is not Not a research-only post. Publications help; shipping is the job. Not a pure platform/SRE hire. Infra is part of the design, not the whole design. Not a prompt engineer who hands off “the architecture” to someone else. Not a people manager of a large team on day one — you influence through artefacts and reviews. How we will assess you We value transparency and respect your time. Our hiring process is designed to evaluate both hands-on technical depth and architectural judgment through practical, real-world tasks. Stage 1: Application & Knockout Screening Submit your CV alongside key screening responses Artefact — send two of: architecture note, eval design, runbook, ADR. Slides without decisions do not count. Stage 2: Initial Conversation (30 mins) An introductory screen covering your background, project context and baseline alignment. An opportunity for you to ask questions about our technical roadmap, partner ecosystem, and vision. Stage 3: Practical Take-Home Written Assessment (2–3 hours) Design exercise on a constrained generation + judge + human-review pipeline (we will use a sanitised exam-authoring brief). We want trust boundaries, eval, and cost, not a model-name dump. Stage 4: Deep-Dive Technical & System Architecture Walkthrough Walkthrough of a system you shipped: what failed in review, what you changed, what it costs to run. Stage 5: Final Stakeholder & Vision Alignment Final conversation with key technical leadership and program stakeholders. Employment Details Function: Solutions / Architecture Reports to: Head of Technology / CTO Expected Start Date: Immediate Location: India (remote-first; travel to client workshops as needed) Experience: 8–13 years (strong 6–8 considered if you have shipped production GenAI) Engagement: Full-time Remuneration: Based on candidate’s experience and skillset Flexibility to work across timezones: Our team spans Latin America to Southeast Asia. Early mornings  or late evenings can be a regular part of the rhythm Why join us? Impact at Scale: Your work will directly support the learning journeys of millions of children. With programs active in 31 countries, you will help build Digital Public Goods that bridge the gap for the millions of children across the Global South who still struggle with foundational literacy Innovation at the intersection of Education and Technology: Lead the charge in redefining learning and learning processes through technology. You will contribute to the design and development of cutting-edge learning tools and AI-driven initiatives, ensuring EdTech isn’t just a buzzword, but a scalable solution for global equity. A Culture of Growth and Evidence: At Pratham, we value rigorous testing and continuous learning. You will have the autonomy to experiment with the latest SOTA models, supported by a 5-year strategic roadmap and funding from partners like the Gates Foundation and Anthropic. High-Calibre, Mission-Driven Team: You will work alongside a global team of experts from institutions like MIT, Harvard, and INSEAD. Our alumni have gone on to lead successful startups and international policy shifts, all while remaining dedicated to the goal of “Every Child in School and Learning Well.” Inclusive and Collaborative Environment: Join a diverse team that spans Asia, Africa, and Latin America. We value innovative thinking and local expertise, ensuring that the technology we build is deeply rooted in the realities of the communities we serve. Pratham International is an equal opportunity employer and encourages all qualified people from diverse backgrounds to apply for positions within our organisation, regardless of race, gender, disability or any other status.

Location: Remote-India About Pratham International Pratham International (PI) is a US 501(c)(3) established to support learning innovations for children and youth in contexts across the globe. We enable the adaptation and spread of proven and scalable education solutions pioneered in India by Pratham Education Foundation, such as Teaching at the Right Level (TaRL), to contexts across the global south through programming and technical assistance tailored to local realities.  Today, Pratham innovations reach millions of children annually through partnerships across ~30 countries. Background Pratham International (PI) works across 30+ countries on foundational learning, with a core team of approximately 65 people in 7 countries. PI is putting in place a structured Internal Capability Building model: a defined set of programs, a delivery approach that works across time zones, and a repeatable cadence for on-boarding new joiners. PI will not build learning content in-house. The model rests on external partners who can design and deliver to a dispersed team. Provider evaluation for the first program in scope, inclusion and cross-cultural sensitisation, is underway and at an early stage. PI is engaging an advisor to complete that assessment, support selection, anchor the first rollout, and leave behind a process PI can run on its own. Scope and deliverables Deliverable 1 — Comprehensive partner assessment Develop an evaluation framework with PI, covering at minimum: –    reach and scheduling across 7 countries and multiple time zones –    blended delivery: self-paced content plus live online sessions –    ability to run small quarterly cohorts, not only one-off large-group programs –    cultural relevance across PI's geographies, and facilitator bench and quality –    platform capability, pricing at PI's likely cohort sizes, and client references Assess the providers PI has identified — Sparkwise, Joy of Performance and affiliates, Guiding Star, Growth Space — and scan additional options, India-based and international, so the assessment is comprehensive rather than limited to inbound leads. Output : comparison matrix, a short written assessment of the shortlist, and a recommendation with rationale and trade-off. Deliverable 2 — Delivery model and partner selection Recommend the delivery model: the split between self-paced and live content, cohort sizing, scheduling logic across time zones, and what “quarterly” looks like in practice. Support selection — structure provider conversations and demos, test the recommendation with stakeholders, and surface the decision for PI sign-off. Support scoping with the selected partner, including what PI owns versus what the partner owns. Output : delivery model note, a selection recommendation ready for decision, and an agreed scope of work with the partner. Deliverable 3 — Anchor the cross-cultural sensitisation rollout Anchor end-to-end delivery of the program to all current members of the PI team. Work with the partner to contextualise content to PI: network structure, entity mix, and the cross-cultural dynamics of PI's teams. Own the rollout mechanics: cohorting, scheduling across time zones, participant communications, leadership framing, completion tracking and follow-up. Output : rollout plan and calendar, participant communications, completion and feedback data, and a short post-rollout report with implications for the quarterly model. Deliverable 4 — Quarterly process for new hires Design the standing process: quarterly windows, enrolment triggers off the joining date, cohort minimums and maximums, ownership within PI's HR and PMO team, tracking, escalation, and indicative annual budget. Map which programs belong in the recurring on-boarding stream alongside cross-cultural sensitisation. Hand over the documentation, templates, and partner-facing routines PI needs to run it without ongoing advisory support. Output : documented process and calendar, templates and trackers, and a handover session with the PI team. Working Arrangement Remote and part-time; hours and days of work to be estimated by mutual agreement. Availability for calls in late India hours, typically 7:00–10:00 pm IST, to overlap with the US East Coast morning. A standing weekly check-in, plus working sessions with the HR and PMO team. Deliverables to be decision-ready rather than discussion-starters. Advisor profile Essential India-based, with availability for late-IST calls. Demonstrated experience designing and rolling out capability-building or L&D programs for globally dispersed teams. Experience with blended delivery, and with evaluating, selecting and managing external learning providers. Strong written outputs that senior stakeholders can decide from. Able to work independently in a lean organisation. Desirable Familiarity with inclusion, DEI or cross-cultural content; nonprofit or international development experience; exposure to LMS platforms. Indicative Timeline Phase 1  [Weeks 1-2] Evaluation framework and provider scan Phase 2  [Weeks 3-4] Assessment, recommendation, selection Phase 3  [Weeks 4-6] Scoping with the selected partner Phase 4  [Weeks 6-8] Cross-cultural sensitisation rollout to full team Phase 5  [Weeks 8-10] Quarterly process design and handover What PI will provide Access to discussions on this topic so far, key stakeholders across teams and levels, plus decision forums for the recommendation. Materials and contacts for the four Partner-candidates already identified. Team roster, geographic distribution, new-hire volumes, and budget parameters. Confidentiality and IP All materials produced under this engagement are the property of Pratham International. Provider pricing, proposals and internal assessments are not to be shared outside PI. The advisor will sign PI's standard confidentiality agreement before starting. Location and Compensation Location: This is a remote role based in India. Compensation: Commensurate with experience. Hiring Note: This position will be hired via our organisational partner in India – PUSA Learning Solutions, LLC. E mployment Details Remit: Global Location: India Start Date: As soon as possible. Pratham is an equal-opportunity employer and encourages people from diverse backgrounds to apply for positions within our organization.