Lead AI Engineer

LeadSquared; Bengaluru; On-site; INR 3,500,000 - 7,000,000; Full time

Job summary

LeadSquared is seeking a hands-on Lead AI Engineer to own our AI stack end to end, covering 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.

You will architect GenAI/LLM solutions, optimize RAG pipelines, fine-tune domain models, and evaluate AI systems for quality and cost. Mentor engineers and collaborate with product teams to drive impact.

Qualifications

• Hands-on experience leading GenAI/LLM initiatives in production.
• Ability to design end-to-end AI stacks with guardrails and safety measures.
• Experience in architecture of multi-agent systems and tool integrations.

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 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.

Skills

GenAI & LLMs

Agentic AI

RAG & vector databases

Fine-tuning

Deep learning

NLP & CV

Evaluation & Responsible AI

Education in CS/AI

Education

Bachelor's in Computer Science/AI/Data Science

Master's in CS/AI

Tools

Pinecone

Weaviate

Qdrant

Milvus

Chroma

FAISS

Job description

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

• 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.
• 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.