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
- Robust 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

Valuable-to-Have Skills
- End-to-End Production AI
- Fine-Tuning
- RAG
- Agentic AI
- LLMOps
- LangGraph / LangChain / LlamaIndex
- Model Evaluation & Observability
- GraphRAG
- PEFT / LoRA / QLoRA

📌 Lead Machine Learning Engineer (Bengaluru)
🏢 Weekday AI (YC W21)
📍 Bengaluru