Role Overview :

As a Principal Machine Learning Engineer, you will serve as a technical authority driving the architecture, strategy, and production deployment of our Generative AI and Agentic ML systems. You will lead technical direction across model orchestration, mixture of experts (MoE), reasoning, retrieval-augmented generation (RAG), and agentic tool use.

Key Responsibilities :

- Technical Leadership : Lead the research, design, and architecture of enterprise-grade GenAI systems, agentic workflows, and fine-tuned LLM/SLM ensembles.

- Core ML Infrastructure : Conceptualize and deploy model architectures spanning reasoning, retrieval, ranking, dialog, and automated code generation.

- Inference Optimization : Optimize inference compute, latency, context utilization, and orchestration patterns for multi-step autonomous agents.

- System Quality & Governance : Establish evaluation benchmarks, hallucination controls, and feedback loops for agent performance in mission-critical environments.

- Mentorship & Strategy : Mentor staff and senior ML engineers, setting technical bars and driving execution aligned with product roadmaps.

Requirements :

- Experience : 10+ years in Machine Learning and Software Engineering, with a track record of shipping ML models to production at enterprise scale.

- Deep Learning & GenAI : Advanced expertise in LLMs, Transformers, RAG pipelines, Vector Databases, and Agentic orchestration frameworks (e.g., LangChain, LlamaIndex, AutoGen, custom harnesses).

- Programming & Tools : Expert in Python, PyTorch/TensorFlow, model fine-tuning (LoRA, QLoRA), vLLM, and cloud ML infrastructure (AWS SageMaker / GCP / Azure).

- Education : B.Tech/M.Tech/Ph.D. in Computer Science, AI, or related quantitative field from a Tier-1 institute (IITs, NITs, IIITs, BITS).