Staff ML Engineer

Weekday AI · Bengaluru, India · Full-time · Posted 2026-09-29

Salary: INR 6,000,000–10,000,000

Workplace: on_site

Department: Weekday's Client via platform

Description

𝗧𝗵𝗶𝘀 𝗿𝗼𝗹𝗲 𝗶𝘀 𝗳𝗼𝗿 𝗼𝗻𝗲 𝗼𝗳 𝘁𝗵𝗲 𝗪𝗲𝗲𝗸𝗱𝗮𝘆'𝘀 𝗰𝗹𝗶𝗲𝗻𝘁𝘀

𝗦𝗮𝗹𝗮𝗿𝘆 𝗿𝗮𝗻𝗴𝗲: 𝗥𝘀 𝟲𝟬𝟬𝟬𝟬𝟬𝟬 - 𝗥𝘀 𝟭𝟬𝟬𝟬𝟬𝟬𝟬𝟬 (𝗶𝗲 𝗜𝗡𝗥 𝟲𝟬-𝟭𝟬𝟬 𝗟𝗣𝗔)

Experience: 13+ yrs

Location: Bengaluru

Job Type: Full-time

We are looking for an experienced Staff ML Engineer – Generative AI to design, build, and scale production-grade GenAI applications and intelligent software systems. The role combines hands-on engineering, AI architecture, technical leadership, and end-to-end ownership of enterprise AI solutions.

The ideal candidate will have strong experience taking GenAI applications beyond prototypes into production, with a focus on reliability, evaluation, observability, security, cost optimisation, user trust, adoption, and measurable business impact.

Requirements

Key Responsibilities

• Design, develop, and launch production-grade GenAI applications including assistants, copilots, document intelligence, workflow automation, and decision-support solutions.
• Identify high-impact opportunities where AI can improve productivity, service quality, operational efficiency, customer experience, or business outcomes.
• Take GenAI applications from concept and experimentation through production deployment and ongoing optimisation.
• Lead hands-on technical execution across application architecture, model selection, prompting, retrieval, orchestration, APIs, data pipelines, and user experiences.
• Architect scalable LLM applications using RAG, agentic workflows, tool use, structured outputs, grounding, and orchestration.
• Evaluate and select appropriate frontier models, open-source models, smaller task-specific models, fine-tuned models, or deterministic approaches based on business requirements.
• Establish practical evaluation frameworks covering accuracy, relevance, groundedness, safety, latency, cost, user trust, adoption, and business impact.
• Build production capabilities for observability, monitoring, versioning, fallback mechanisms, privacy, security, reliability, and operational ownership.
• Analyse production feedback and continuously improve AI application quality, performance, reliability, and user experience.
• Work with cross-functional stakeholders to define requirements, establish success criteria, and measure real-world impact.
• Stay current with emerging GenAI technologies and pragmatically evaluate techniques that improve quality, speed, scalability, or cost efficiency.
• Contribute to engineering standards, technical architecture decisions, AI development practices, and responsible AI implementation.
• Mentor engineers and provide technical leadership across complex AI application initiatives.

What Makes You a Great Fit

• 13+ years of experience building applied AI/ML-based intelligent software systems, with strong hands-on engineering expertise.
• 3+ years of practical GenAI application experience, including production applications used by real users at meaningful scale.
• Proven experience taking GenAI solutions from PoC/prototype to production, with ownership of reliability, launch quality, cost, user feedback, adoption, and measurable impact.
• Strong understanding of modern LLM application architectures including RAG, agents, tool use, structured outputs, retrieval, grounding, and orchestration.
• Experience with LangGraph, LangChain, LlamaIndex, and LLM APIs such as GPT, Claude, or Gemini.
• Strong programming and software engineering capabilities, with the ability to build production-ready AI applications rather than only prototypes.
• Experience implementing evaluation and observability frameworks using tools such as Langfuse, Arize, or similar platforms.
• Strong understanding of enterprise AI requirements including security, privacy, reliability, monitoring, cost management, and user trust.
• Experience with AI-native development tools such as Cursor, Claude Code, or similar tools is preferred.
• Strong architectural judgement with the ability to balance model capabilities, application complexity, performance, cost, and reliability.
• Experience with advanced AI techniques such as GraphRAG, long-context architectures, model routing, caching, cascades, PEFT/LoRA/QLoRA, knowledge distillation, or open-source model deployment is an advantage.
• Strong analytical, problem-solving, communication, and cross-functional collaboration skills.
• Ability to operate effectively in ambiguous, fast-moving environments and take end-to-end ownership of complex technical initiatives.
• Strong interest in building trustworthy, scalable, measurable, and production-ready AI systems.

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