Salary
≈ $25k – $66k per year (Estimated)
Location
In office (Bengaluru)
Seniority
Staff · 4+ years exp
Employment
Full-Time
First seen by Alion on Oct 27, 2026.
Overview
Company
Impact
Profile match
LeadSquared provides sales execution and marketing automation software. Its platform manages high-volume lead capture, distribution and field sales activity. Education, healthcare and financial services companies are core customers.
Job Description – Lead AI/ML Engineer
14 Oct 2026
We are hiring a hands-on Lead AI/ML Engineer to design, build and ship production AI systems, from agentic LLM applications to classical ML models. You will own solutions end to end, guide a small team, and turn business problems into reliable AI products.
Key Responsibilities
14 Oct 2026
- @Shruti
- Type: Full-time
- Location: Bengaluru
We are hiring a hands-on Lead AI/ML Engineer to design, build and ship production AI systems, from agentic LLM applications to classical ML models. You will own solutions end to end, guide a small team, and turn business problems into reliable AI products.
Key Responsibilities
- Design and build agentic AI systems with planning, tool calling, memory and multi-agent workflows using LangChain and LangGraph.
- Architect and optimise RAG pipelines: 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.
- 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.
- LLM fine-tuning (LoRA/QLoRA), LLM evaluation tools (LangSmith, Ragas) and MLOps tools (MLflow, SageMaker).
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