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.