Machine Learning Engineer - 2

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 experiencedAI/ML Engineerto build and own production-gradeMachine Learning and Generative AI systemsend-to-end. The role focuses on developing intelligent applications usingLLMs, RAG, conversational AI, agentic workflows, personalization, recommendations, memory, and user intelligence.

The ideal candidate will combine strongPython and software engineering fundamentalswith 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 ownproduction-grade ML/AI systemsacross the complete development lifecycle.
• Build and integrateLLM-powered applications, including RAG pipelines, conversational AI, and agentic workflows.
• Develop retrieval systems usingembeddings, vector search, semantic retrieval, and context enrichment.
• Build AI capabilities forpersonalization, memory, recommendations, and user intelligence.
• Design LLM orchestration workflows to coordinate models, tools, retrieval systems, and application logic.
• Develop evaluation frameworks to measureLLM quality, accuracy, relevance, reliability, latency, and cost.
• Optimize AI systems for production performance, scalability, response quality, and resource efficiency.
• Combine structured domain intelligence withML, retrieval, and LLM reasoningto 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 emergingLLMs, 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, fromdesign and implementation through evaluation, deployment, and production support.

What Makes You a Great Fit

• 3+ years of experiencein Machine Learning, Applied ML, NLP, Generative AI, or AI engineering.
• Strong proficiency inPythonwith solid software engineering and programming fundamentals.
• Hands-on experience building applications usingLLMs, RAG, embeddings, vector search, or conversational AI.
• Proven experience deploying and supportingML/AI systems in production.
• Strong understanding of machine learning fundamentals, model evaluation, experimentation, and performance optimization.
• Experience designing and developingAI 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 forquality, 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 withLangChain or LangGraphis an advantage.
• Familiarity with vector databases and technologies such asPinecone, Weaviate, Milvus, pgvector, or similaris desirable.
• Experience withHugging Face and open-source LLMsis a plus.
• Knowledge ofMLOps, LLM evaluation frameworks, recommendation systems, or multilingual/Indic NLPis 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.