Senior Machine Learning Engineer

Key Responsibilities

• Design, build, and ship production-grade LLM applications: RAG pipelines, agentic workflows,

and tool-calling systems for education-specific use cases such as question answering, content

generation, adaptive feedback, and curriculum alignment.

• Own full system architecture: data pipelines, retrieval layers, orchestration, APIs, and service

infrastructure written as maintainable, tested production code, not notebooks or scripts.

• Build rigorous LLM evaluation frameworks: task-specific benchmarks, regression testing, human

eval loops, and automated quality gates to catch degradation before it ships.

• Own latency and cost at the application layer: caching strategies, request batching, model/route

selection, and prompt-context optimisation to keep production systems fast and affordable at

scale.

• Build data pipelines for grounding and instruction data, including multilingual and Indic language

sources.

• Assess new open-source and closed model releases for domain applicability, cost, and

production readiness.

• Define and track system performance metrics, evaluation benchmarks, and reliability targets

across all shipped features.

• Work with open-source and sovereign LLMs, integrating them into production-proven serving and

orchestration frameworks.

• Where warranted, fine-tune or adapt foundation models (LoRA/QLoRA/SFT) a strong plus, not a

gating requirement for this role.

Must-Have Skills

• Strong production Python engineering: clean, tested, maintainable code — not scripts or

notebooks. Comfortable owning services in production.

• Hands-on experience building and shipping RAG or agentic systems in production: retrieval

design, orchestration (e.g. LangChain/LangGraph or equivalent), tool calling, and multi-step

reasoning pipelines.

• Demonstrated experience building LLM evaluation systems: benchmarks, regression tests,

human-eval workflows, and quality monitoring — not just anecdotal "it works."