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.
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