Sr. ML Engineer - Generative AI

AI Platform

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."

- Experience building and operating backend systems/services: APIs, data pipelines, deployment, and monitoring in a real production environment.

- Working knowledge of AI/ML pipelines, data preparation, and model deployment.

- Experience working with open-source and sovereign LLMs, using standard, production-proven frameworks.

- Understanding of cloud platforms and infrastructure for serving ML/LLM systems at scale.

Good to Have :

- Hands-on fine-tuning experience with LoRA, QLoRA, SFT, DPO, or similar preference-optimisation techniques.

- Familiarity with DeepSpeed, FSDP, or large-scale distributed training frameworks.

- Experience with inference optimisation (quantisation, ONNX, efficient serving) at the model layer.