AI/ML Solution Architect
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Job Description
AI/ML Solution Architect designing secure, scalable AI assessment platforms for Pratham International’s global education programs. Architecting RAG, LLM judging, OCR, cloud, and LLMOps systems.
Responsibilities:
- Own solution design from initial workshop through delivery-ready blueprint
- Lead solution workshops with programme, ministry, engineering, and external stakeholders
- Convert requirements into service boundaries, data stores, sequence diagrams, decision gates, and open-question lists
- Write architecture decision records and resolve conflicting technical decisions
- Architect LLM generation, curriculum knowledge-graph retrieval, LLM-as-judge pipelines, and constraint-based paper assembly
- Design human-AI workflows with auditability, segregation of duties, cycle-pinned configuration, and protection of student PII
- Choose and govern models, prompts, evaluation gates, fallbacks, and OCR providers
- Shape Azure/AWS platform architecture including identity, networking, LLMOps/MLOps, observability, and sovereign hosting
- Define contracts, schemas, metadata logging, evaluation harnesses, release gates, rollback, cost controls, and FinOps guardrails
- Stay with systems through build and first production cycle
- Produce POVs, architecture packs, sequence packs, workshop artefacts, and reusable country adaptation patterns
- Mentor technical leads on boundaries between model-based logic and deterministic workflow logic
Requirements:
- 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 at scale that passed real security, cost, and operations review
- Ability to produce delivery-ready architecture blueprints covering services, data stores, IAM, failure modes, evaluation strategy, and cost model
- Deep cloud experience on Azure and/or AWS
- Experience defending landing-zone and private-endpoint architecture and real compute/inference/OCR spend
- Fluent in Python
- Hands-on experience with classical ML, model training, evaluation, feature pipelines, LLM APIs, embeddings, and vector search under production load
- Comfortable running workshops with non-engineers and translating between technical and non-technical stakeholders
- Strong written English
- Flexibility to work across time zones, including regular early mornings or late evenings
- Strongly preferred: Azure AI plus AWS or GCP; LLMOps/MLOps; document AI/OCR at volume; structured-knowledge RAG and hybrid search; education, assessment, public-sector, health, finance, or identity domains; relevant cloud certifications; agent security; FinOps
- Nice to have: Microsoft Fabric, Databricks, Semantic Kernel/Microsoft Agent Framework, MCP, constraint solvers, exam-blueprint/item-bank systems, multilingual generation, golden/eval datasets, government data systems, data-sharing agreements, or sovereign hosting experience
Benefits:
- Impact at scale supporting learning journeys of millions of children
- Autonomy to experiment with the latest SOTA models
- Support from a 5-year strategic roadmap and funding from partners like the Gates Foundation and Anthropic
- High-calibre, mission-driven global team
- Inclusive and collaborative environment
- Equal opportunity employer
- Remote-first work arrangement
- Travel to client workshops as needed
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