Lead Machine Learning Engineer

Key Responsibilities :

- Design, build, and deploy applications powered by Large Language Models (LLMs).

- Fine tuning of LLM, SLMs & building from scratch.

- Develop and optimize prompt engineering strategies to improve model performance and reliability.

- Build Retrieval-Augmented Generation (RAG) pipelines using vector databases and enterprise knowledge sources.

- Design and implement Agentic AI workflows capable of multi-step reasoning, planning, and tool usage.

- Integrate LLMs with APIs, databases, enterprise systems, and external tools.

- Fine-tune and customize foundation models for domain-specific use cases.

- Develop evaluation frameworks to measure accuracy, latency, hallucination rates, and user experience.

- Collaborate with Product, Engineering, and Data teams to translate business requirements into AI-powered solutions.

- Ensure scalability, security, observability, and cost optimization of AI systems.

- Stay updated with advancements in Generative AI, Agentic AI, and emerging LLM technologies.

Required Skills & Qualifications :

Technical Skills :

- Strong proficiency in Python and software engineering best practices.

- Experience working with LLMs such as GPT, Claude, Gemini, Llama, or similar models.

- Hands-on experience with Prompt Engineering and Prompt Optimization.

- Strong understanding of Retrieval-Augmented Generation (RAG) architectures.

- Experience with vector databases such as Pinecone, Weaviate, Chroma, Milvus, or FAISS.

- Familiarity with LLM orchestration frameworks such as LangChain, LlamaIndex, CrewAI, AutoGen, or similar.

- Experience integrating REST APIs, databases, and external tools into AI workflows.

- Knowledge of cloud platforms such as AWS, Azure, or GCP.

- Understanding of NLP, Machine Learning, and AI system design principles.

Preferred Skills :

- Experience fine-tuning open-source LLMs.

- Knowledge of AI evaluation frameworks and benchmarking methodologies.

- Familiarity with MLOps, CI/CD pipelines, Docker, and Kubernetes.

- Experience building multi-agent systems and autonomous AI agents.

- Exposure to speech AI, voice assistants, or conversational AI platforms.