Job
Description
About The Opportunity We operate at the forefront of India’s Artificial Intelligence & Enterprise Software Solutions sector, building production-grade, large-language-model (LLM) applications that power real-time search, recommendation, and decision-support systems for Fortune-500 clients. Our fully remote engineering pods in Mumbai and Pune transform cutting-edge GenAI research into scalable business value while nurturing a culture of ownership, learning, and rapid iteration. Role & Responsibilities Design and ship GenAI products that fuse Retrieval-Augmented Generation (RAG) with LangChain/LangGraph pipelines for chatbots, semantic search, and agentic workflows. Implement vector-based retrieval by orchestrating FAISS-backed indexes, chunking strategies, and prompt-engineering playbooks that boost LLM precision and recall. Prototype and harden ML models (classification, regression, clustering) in Scikit-learn or PyTorch, then productionise via micro-checkpointing (MCP) and CI/CD. Instrument agentic behaviours that call external tools/APIs, manage memory, and evaluate reasoning traces for safety and ROI. Collaborate cross-functionally with product, design, and MLOps to translate business stories into measurable AI metrics and A/B experiments. Author technical docs & knowledge share to uplevel team expertise in GenAI best practices and responsible-AI compliance. Skills & Qualifications Must-Have 3–7 yrs hands-on experience building LLM-powered applications with LangChain and/or LangGraph. Proven mastery of FAISS (or Pinecone/Weaviate) for vector search, plus solid understanding of embeddings and cosine-similarity maths. Strong foundation in machine-learning algorithms—classification, regression, and model evaluation—with production code in Scikit-learn or equivalent. Ability to craft, debug, and optimise prompt engineering & chunking strategies that minimise token cost while maximising answer quality. Fluency in Python; familiarity with software-engineering best practices (Git, unit tests, Docker, MCP-style model checkpoints). Excellent written and verbal communication skills to explain complex GenAI concepts to technical and non-technical stakeholders. Preferred Experience designing agentic frameworks (tool-calling, planning-&-execution loops, reflection) for autonomous task chains. Prior contribution to open-source GenAI libraries or research publications. Exposure to data-pipeline tooling such as Airflow, Spark, or cloud-agnostic serverless runtimes. Skills: GenAI,LangChain,LLM,LangGraph,FAISS,MCP,Agentic,Machine Learning,Classification,Regression,ScikitLearn Show more Show less
