Lead AI/ML Engineer
LeadSquared
- Bangalore, Karnataka, India
- 4+ years
- Bachelor's degree
- Posted
This job is closed: the employer no longer lists it.
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).
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