Lead Machine Learning Engineer

Role details

Weekday - Lead Machine Learning Engineer Lead Machine Learning Engineer Bengaluru Technology, Information and Media – Technology, Information and Media / Full-time / On-site apply for this job This role is for one of our clients Industry: Software Development Seniority level: Mid-Senior level Min Experience: 9+ years Location: Bengaluru JobType: full-time ₹40,00,000 - ₹80,00,000 a year We are seeking a hands-on Lead Machine Learning Engineer to design, build, and scale production-grade Generative AI and Machine Learning applications . The role will focus on developing AI-powered assistants, retrieval and reasoning systems, agentic workflows, document intelligence, decision-support solutions, and intelligent automation capabilities that improve productivity, service quality, customer experience, and business outcomes. This is a technical leadership role for an engineer who has moved beyond experimentation and prototypes and has proven experience taking AI applications through the last mile into production . You will be responsible for ensuring AI systems are reliable, observable, secure, cost-efficient, measurable, and trusted by users. You will work closely with Product, Engineering, Design, Security, Compliance, Operations, and business stakeholders to identify high-impact AI opportunities, make pragmatic architecture decisions, and deliver production-ready AI experiences at scale. Requirements Key Responsibilities Build AI Solutions for Business Impact Design, build, and launch GenAI-powered applications including AI assistants, copilots, document intelligence, workflow automation, and decision-support solutions. Identify high-impact opportunities where AI can improve productivity, operational efficiency, service quality, customer experience, and business outcomes. Take AI applications from concept through production, collaborating with Product, Engineering, Design, Security, and business teams. Lead hands-on technical execution across application architecture, model selection, prompt engineering, retrieval, orchestration, APIs, data pipelines, and user-facing experiences. Translate business requirements into scalable and measurable machine learning and AI solutions. Establish success metrics and continuously optimize solutions based on real-world user feedback and business impact. Build Enterprise-Grade AI Systems Architect reliable GenAI applications using modern approaches such as RAG, agentic workflows, tool use, structured outputs, retrieval, grounding, and fine-tuning where appropriate. Design systems that effectively combine frontier models, open-source models, smaller task-specific models, and deterministic components based on the specific use case. Develop strong grounding mechanisms using enterprise knowledge and relevant business data. Build production systems with appropriate observability, monitoring, versioning, fallback mechanisms, security, privacy, and operational ownership. Design for reliability, scalability, latency, cost efficiency, and maintainability. Stay current with advances in AI/ML and apply emerging techniques pragmatically where they deliver meaningful improvements. Evaluation, Quality & LLMOps Define practical evaluation frameworks for GenAI applications covering accuracy, relevance, groundedness, safety, latency, cost, user trust, adoption, and business impact. Establish automated and human-in-the-loop evaluation processes for AI applications. Use LLM evaluation and observability platforms such as LangFuse, Arize, or similar tools . Monitor production performance and identify opportunities to improve model quality, reliability, and efficiency. Establish appropriate safeguards, fallback paths, and quality controls for production AI systems. Technical Leadership Provide technical leadership across the AI/ML application development lifecycle. Make pragmatic architecture and technology decisions while balancing quality, speed, security, and cost. Mentor engineers and contribute to engineering standards, best practices, and technical direction. Partner with cross-functional teams to ensure AI solutions are usable, secure, reliable, and aligned with business objectives. Take ownership of production outcomes, including launch quality, reliability, user feedback, adoption, and measurable impact. Required Experience & Qualifications 8+ years of experience building applied AI/ML-based intelligent software systems. 2+ years of practical Generative AI application experience . At least one production GenAI application that has been deployed to real users at meaningful scale. Proven experience taking GenAI solutions beyond PoC/prototype into production. Strong ownership of production quality, reliability, cost optimization, user feedback, adoption, and measurable business impact. Strong understanding of designing LLM applications using an appropriate combination of: RAG Agentic workflows Tool use Structured outputs Retrieval and grounding LLM orchestration Frontier and open-source models Fine-tuning Task-specific models Deterministic systems Experience with modern AI application frameworks and LLMOps tools such as LangGraph, LangChain, LlamaIndex, and leading LLM APIs . Strong programming and software engineering capabilities with the ability to build and deploy production-quality AI applications. Experience using AI-native development tools such as Cursor, Claude Code, or similar tools is preferred, with strong judgment around code quality, security, and production reliability. Good-to-Have Experience GraphRAG Long-context architectures Model routing Semantic and intelligent caching Model cascades PEFT / LoRA / QLoRA Knowledge retrieval and grounding Model distillation Open-source model deployment Advanced LLM evaluation and observability Enterprise AI security and governance Must-Have Skills Machine Learning Generative AI (GenAI) Production AI/ML Systems LLM Applications Python / Software Engineering AI Application Architecture Good-to-Have Skills End-to-End Product