LeadSquared
Lead AI Engineer
- Compensation
- Not disclosed
- Location
- Bangalore, Karnataka, India
- Work setup
- Not specified
- Seniority
- Lead
- Employment
- Full time
- Experience
- 4+ years
- Category
- Ai engineering
- Posted
Skills
Must have
- 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.
Full description
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.