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Lead AI/ML Engineer

LeadSquared

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Skills

Required

  • ML
  • AI
  • LLM / GenAI
  • Python
  • NumPy
  • Pandas
  • FastAPI
  • async
  • Pinecone / Weaviate / Qdrant / Milvus / Chroma / FAISS
  • scikit-learn
  • PyTorch
  • TensorFlow
  • Git
  • Docker
  • AWS / Azure / GCP

Preferred

  • planning
  • tool calling
  • multi-agent workflows
  • chunking
  • embeddings
  • deep learning models
  • cloud
  • Lead AI/ML Engineer 14 Oct 2026 · @Shruti Experience: 4–6 years · Type: Full-time · Location: Bengaluru About the role We are hiring a hands-on
  • AI/ML Engineer to design, build and ship production AI systems, from agentic LLM applications to classical ML models.

Responsibilities

  • You will own solutions end to end, guide a small team, and turn business problems into reliable AI products.
  • Design and build agentic AI systems with planning, tool calling, memory and multi-agent workflows using LangChain and LangGraph.
  • chunking, embeddings, vector search, re-ranking and evaluation.
  • Build, train and deploy traditional ML and deep learning models with scikit-learn, PyTorch and TensorFlow.
  • Write production-grade Python, expose models as APIs, and deploy on cloud with MLOps best practices.
  • Lead and mentor ML/AI engineers, review code and designs, and partner with product and business teams.

Requirements

  • Required skills 4–6 years of overall experience, including at least 1–2 years building LLM or GenAI applications.
  • Expert-level Python (NumPy, Pandas, FastAPI, async).
  • Hands-on experience with Agentic AI, LangChain, LangGraph and RAG.
  • Practical experience with vector databases such as Pinecone, Weaviate, Qdrant, Milvus, Chroma or FAISS.
  • Strong traditional ML skills with scikit-learn, PyTorch and TensorFlow, plus solid statistics and model evaluation.
  • Working knowledge of Git, Docker and a cloud platform (AWS, Azure or GCP).
  • Bachelor's or master's degree in computer science, AI, Data Science or a related field.
  • Nice to have LLM fine-tuning (LoRA/QLoRA), LLM evaluation tools (LangSmith, Ragas) and MLOps tools (MLflow, SageMaker).