Lead AI Engineer

Job Description – Lead AI Engineer
14 Oct 2026 · @Shruti
Experience: 4–6 years · Type: Full-time · Location: Bengaluru
About the role
We are hiring a hands-on Lead AI Engineer to own our AI stack end to end: generative AI, agentic systems, deep learning, NLP and computer vision. You will set technical direction, lead a small team, and ship AI products that are reliable, safe and measurable in production.
Key responsibilities
Architect and build GenAI and agentic AI solutions: multi-agent workflows, tool calling, memory and human-in-the-loop.
Design, optimise and evaluate RAG pipelines and vector search for accurate, grounded answers.
Fine-tune and adapt LLMs and other foundation models for domain-specific tasks.
Build and deploy deep learning, NLP and computer vision models for prediction, classification and automation.
Evaluate AI systems and optimise them for quality, cost and latency.
Implement guardrails, security and responsible-AI practices (bias, privacy, hallucination and prompt-injection controls).
Lead and mentor AI engineers, review code and designs, and partner with product and business teams on use cases and success metrics.
Required skills
Programming: Expert Python (NumPy, Pandas, FastAPI, async); SQL.
Generative AI and LLMs: OpenAI, Anthropic Claude, Gemini, Llama and Mistral; prompt engineering, structured outputs and function calling.
Agentic AI: LangChain, LangGraph, LlamaIndex, CrewAI or AutoGen; MCP (Model Context Protocol) and tool integrations.
RAG and vector databases: Chunking, embeddings, hybrid search and re-ranking; Pinecone, Weaviate, Qdrant, Milvus, Chroma, FAISS or pgvector.
Fine-tuning: Hugging Face Transformers, LoRA/QLoRA, PEFT and RLHF/DPO basics.
Deep learning: PyTorch and TensorFlow/Keras; CNNs, RNNs and Transformers.
NLP and computer vision: Text classification, NER, summarisation; OpenCV, YOLO and vision-language models.
Evaluation and responsible AI: Ragas or DeepEval, guardrails, bias and safety testing.
Cloud: AWS (SageMaker, Bedrock), Azure (Azure OpenAI, Azure ML) or GCP (Vertex AI).
Education: Bachelor's or Master's in Computer Science, AI, Data Science or a related field.
Nice to have
Knowledge graphs or graph RAG, speech AI (ASR/TTS), multimodal models, edge or on-device inference, and open-source contributions or publications.

Experience
4 - 6 Years

Employment type
Full Time

Work arrangement
No