๐ง๐ต๐ถ๐ ๐ฟ๐ผ๐น๐ฒ ๐ถ๐ ๐ณ๐ผ๐ฟ ๐ผ๐ป๐ฒ ๐ผ๐ณ ๐๐ต๐ฒ ๐ช๐ฒ๐ฒ๐ธ๐ฑ๐ฎ๐'๐ ๐ฐ๐น๐ถ๐ฒ๐ป๐๐
๐ฆ๐ฎ๐น๐ฎ๐ฟ๐ ๐ฟ๐ฎ๐ป๐ด๐ฒ: ๐ฅ๐ ๐ฒ๐ฌ๐ฌ๐ฌ๐ฌ๐ฌ๐ฌ - ๐ฅ๐ ๐ญ๐ฌ๐ฌ๐ฌ๐ฌ๐ฌ๐ฌ๐ฌ (๐ถ๐ฒ ๐๐ก๐ฅ ๐ฒ๐ฌ-๐ญ๐ฌ๐ฌ ๐๐ฃ๐)
Experience: 13+ yrs
Location: Bengaluru
Job Type: Full-time
We are looking for an experienced Staff ML Engineer โ 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.