Staff ML Engineer

Weekday 1; Bengaluru; On-site; INR 6,000,000 - 10,000,000; Full time

Job summary

Weekday 1's client is seeking an experienced Staff ML Engineer – Generative AI in Bengaluru to design, build, and scale production-grade GenAI applications. You will own end-to-end AI system delivery, from architecture to deployment, with a strong emphasis on reliability, security, and measurable business impact.

The role requires leading the development of GenAI solutions at scale, ensuring cost efficiency, user trust, and robust observability, while mentoring engineers and driving best

Qualifications

• 13+ years of experience building applied AI/ML-powered software systems.
• 3+ years of practical GenAI application experience, including production-scale systems.
• Experience taking GenAI from PoC 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.
• 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.
• Strong architectural judgement balancing 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 a plus.
• 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.

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.

Skills

GenAI concepts

Production AI engineering

LLM architectures

RAG architectures

LangChain

LangGraph

LlamaIndex

LLM APIs

Observability

Security & privacy

Cost optimization

Mentoring

Tools

Langfuse

Arize

Cursor

Claude Code

Job description

This role is for one of the Weekly's clients

Salary range: Rs 6000000 - Rs 100000000 (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.