[Hiring] AI/ML Solution Architect @Pratham International

Role Description

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.

Qualifications

• 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.

Requirements

• 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.

Benefits

• Your work will directly support the learning journeys of millions of children.
• Lead the charge in redefining learning and learning processes through technology.
• 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.
• You will work alongside a global team of experts from institutions like MIT, Harvard, and INSEAD.
• Join a diverse team that spans Asia, Africa, and Latin America.