ML Engineer - II

𝗧𝗵𝗶𝘀 𝗿𝗼𝗹𝗲 𝗶𝘀 𝗳𝗼𝗿 𝗼𝗻𝗲 𝗼𝗳 𝘁𝗵𝗲 𝗪𝗲𝗲𝗸𝗱𝗮𝘆'𝘀 𝗰𝗹𝗶𝗲𝗻𝘁𝘀 𝗦𝗮𝗹𝗮𝗿𝘆 𝗿𝗮𝗻𝗴𝗲: 𝗥𝘀 𝟮𝟬𝟬𝟬𝟬𝟬𝟬 - 𝗥𝘀 𝟯𝟱𝟬𝟬𝟬𝟬𝟬 (𝗶𝗲 𝗜𝗡𝗥 𝟮𝟬-𝟯𝟱 𝗟𝗣𝗔) Experience: 3+ yrs Location: Bengaluru Job Type: Full-time We are looking for an experienced AI/ML Engineer to build and own production-grade Machine Learning and Generative AI systems end-to-end. The role focuses on developing intelligent applications using LLMs, RAG, conversational AI, agentic workflows, personalization, recommendations, memory, and user intelligence . The ideal candidate will combine strong Python and software engineering fundamentals with hands-on experience building, evaluating, deploying, and optimizing AI systems for real-world applications. You will work across ML, retrieval, LLM orchestration, and scalable backend systems to deliver reliable and impactful AI-powered experiences. Requirements Key Responsibilities Design, develop, and own production-grade ML/AI systems across the complete development lifecycle. Build and integrate LLM-powered applications , including RAG pipelines, conversational AI, and agentic workflows. Develop retrieval systems using embeddings, vector search, semantic retrieval, and context enrichment . Build AI capabilities for personalization, memory, recommendations, and user intelligence . Design LLM orchestration workflows to coordinate models, tools, retrieval systems, and application logic. Develop evaluation frameworks to measure LLM quality, accuracy, relevance, reliability, latency, and cost . Optimize AI systems for production performance, scalability, response quality, and resource efficiency. Combine structured domain intelligence with ML, retrieval, and LLM reasoning to deliver context-aware outputs. Build and maintain APIs and production services that integrate AI capabilities with backend systems. Design scalable ML/AI architectures suitable for high-volume production environments. Develop experiments, prototypes, and proof-of-concepts and transition successful solutions into production. Implement monitoring, evaluation, debugging, and continuous improvement processes for deployed AI systems. Collaborate with Product, Backend, and cross-functional engineering teams to deliver AI-powered features. Evaluate emerging LLMs, open-source models, retrieval techniques, agent frameworks, and AI tooling . Contribute to engineering standards, technical documentation, model evaluation practices, and AI system design. Take ownership of problems end-to-end, from design and implementation through evaluation, deployment, and production support . What Makes You a Great Fit 3+ years of experience in Machine Learning, Applied ML, NLP, Generative AI, or AI engineering. Strong proficiency in Python with solid software engineering and programming fundamentals. Hands-on experience building applications using LLMs, RAG, embeddings, vector search, or conversational AI . Proven experience deploying and supporting ML/AI systems in production . Strong understanding of machine learning fundamentals, model evaluation, experimentation, and performance optimization. Experience designing and developing AI APIs, scalable services, and production-ready systems . Strong understanding of system design, scalability, reliability, and cloud-based application development. Experience evaluating and optimizing LLM applications for quality, latency, cost, and reliability . Strong understanding of retrieval pipelines, prompt engineering, context management, and LLM orchestration. Ability to independently own technical problems across the complete lifecycle: design → build → evaluate → deploy → improve . Experience with LangChain or LangGraph is an advantage. Familiarity with vector databases and technologies such as Pinecone, Weaviate, Milvus, pgvector, or similar is desirable. Experience with Hugging Face and open-source LLMs is a plus. Knowledge of MLOps, LLM evaluation frameworks, recommendation systems, or multilingual/Indic NLP is an advantage. Strong analytical and problem-solving skills with a practical, experimentation-driven approach. Excellent communication and collaboration skills with the ability to work effectively across Product and Engineering teams. Strong ownership mindset and interest in building reliable, scalable, and user-focused AI products.