Experience:

• 2–5+ years of experience in software engineering, ML engineering, or a related field
• Hands-on experience building applications using LLMs (GPT, Claude, Llama, Mistral, Gemini, etc.)
• Practical experience implementing RAG systems, including embeddings and vector search
• Strong Python skills with ability to design and ship production-quality code independently
• Familiarity with ML/AI libraries (PyTorch, TensorFlow, Hugging Face Transformers, scikit-learn)
• Experience with LLM orchestration frameworks
• Experience with cloud platforms (AWS, GCP, or Azure) and containerization (Docker/Kubernetes)
• Familiarity with API integration and building scalable backend services

Responsibilities:

• Design, implement, and optimize RAG pipelines including chunking strategies, embeddings, vector databases, retrieval tuning, and re-ranking
• Integrate and fine-tune LLMs (open-source and proprietary APIs) for specific use cases
• Build and maintain prompt engineering frameworks, evaluation harnesses, and testing pipelines to measure output quality, hallucination rates, and latency
• Develop agentic workflows and tool-use integrations including function calling, multi-step reasoning, and orchestration
• Own the full lifecycle of LLM features including data preparation, model selection, deployment, monitoring, and iteration
• Work with vector databases and manage embedding pipelines at scale
• Collaborate with product and data teams to translate business requirements into AI-powered features
• Implement guardrails, safety filters, and evaluation metrics to ensure reliable and responsible AI outputs
• Optimize for cost, latency, and performance across inference infrastructure
• Write clean, well-tested, production-grade Python code
• Stay current with the fast-moving LLM landscape and evaluate new models, tools, and techniques

Nice to have:

• Experience fine-tuning or instruction-tuning open-source LLMs
• Knowledge of evaluation frameworks such as RAGAS, TruLens, DeepEval, or custom evaluation pipelines
• Experience with agentic architectures and multi-agent systems
• Background in MLOps / LLMOps practices including model versioning, monitoring, and CI/CD for ML
• Understanding of AI safety, responsible AI practices, and data privacy considerations
• Experience with streaming inference and real-time chat applications

More skills:

Large Language Models (LLMs), Retrieval-Augmented Generation (RAG) pipelines, Python, PyTorch, TensorFlow, Hugging Face Transformers, scikit-learn, LLM orchestration frameworks, Cloud platforms (AWS, GCP, Azure), Docker, Kubernetes, API integration, FastAPI, Flask, REST/GraphQL APIs, gRPC, Git, GitHub Actions, MLflow, DVC, CrewAI, AutoGen, Pinecone, Weaviate, Qdrant, Milvus, pgvector, Chroma, FAISS, LoRA/QLoRA, PEFT, RLHF, DeepSpeed, TruLens, DeepEval, LangSmith, Weights & Biases, Arize, PromptLayer, Apache Spark, Airflow, Pandas, NumPy, Terraform

Other:

• Pay range: ₹450,000.00 - ₹900,000.00 per year
• Health insurance
• Leave encashment
• Paid sick time
• Paid time off
• Provident Fund
• Department: Engineering / AI & Machine Learning
• Employment Type: Full-time
• Selection advantage for candidates with strong Python coding ability demonstrating software engineering fundamentals including clean architecture, testing, debugging, and performance optimization