Senior Machine Learning Engineer

The LHR Group; Dadri; On-site; INR 4,000,000 - 8,000,000; Full time

Job summary

The LHR Group is seeking a Sr. ML Engineer - Generative AI in India to design and deploy production-grade LLM systems for personalized learning in Indian languages.

You will own RAG pipelines, agentic workflows, evaluation, and backend services with strong software engineering discipline. We expect hands-on execution in a 0-to-1 environment, with responsibility for latency, cost, data pipelines, model evaluation, and integration of open-source and sovereign LLMs at scale.

Qualifications

• Hands-on Python production engineering with maintainable production code.
• Experience building RAG or agentic systems in production: retrieval design and orchestration.
• Experience building LLM evaluation systems with benchmarks and human eval workflows.
• Experience operating backend systems/services: APIs, data pipelines, deployment, monitoring.
• Understanding of AI/ML pipelines, data preparation, and model deployment.
• Experience with open-source and sovereign LLMs in production contexts.
• Knowledge of cloud platforms for serving ML/LLM at scale.

Responsibilities

• Design, build, and ship production-grade LLM applications: RAG pipelines, agentic workflows, and tool‑calling systems.
• Own full system architecture: data pipelines, retrieval layers, orchestration, APIs, and service infrastructure.
• Build rigorous LLM evaluation frameworks with benchmarks, regression tests, and human eval loops.
• Own latency and cost at the application layer with caching, batching, and model routing decisions.
• Build data pipelines for grounding and multilingual/Indic language sources.
• Assess new model releases for domain applicability, cost, and production readiness.
• Define and track system performance metrics and reliability targets.
• Work with open-source and sovereign LLMs, integrating into production serving frameworks.
• Fine-tune or adapt foundation models (LoRA/QLoRA/SFT) where applicable.

Skills

Python production engineering

RAG/agentic systems

LLM evaluation systems

Backend systems & APIs

AI/ML pipelines

Open-source LLMs

Cloud platforms for ML/LLM

Tools

LangChain/LangGraph or equivalent

Job description

As a Sr. ML Engineer - Generative AI, you will build and deploy production-grade LLM systems for personalised learning in Indian languages. You will own RAG, agentic pipelines, evaluation, and backend services, with a strong focus on software engineering. The role offers high ownership in a 0-to-1 environment, requiring hands‑on execution, adaptability, and comfort with ambiguity.

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.