# Lead Machine Learning Engineer > Weekday AI · Bengaluru, India · Full-time · Posted 2026-09-22 **Workplace:** on_site **Department:** Weekday's Client via platform ## Description **This role is for one of Weekday’s clients** Min Experience: 9+ years Location: Bengaluru JobType: full-time 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 Production AI** - **Fine-Tuning** - RAG - Agentic AI - LLMOps - LangGraph / LangChain / LlamaIndex - Model Evaluation & Observability - GraphRAG - PEFT / LoRA / QLoRA ## Apply [Apply at Weekday AI](https://apply.workable.com/weekday-1/j/7F296A04C2/apply) --- Powered by [Workable](https://www.workable.com)