Lead Machine Learning Engineer

Weekday (YC W21); Bengaluru; On-site; INR 4,000,000 - 6,000,000; Full time

Job summary

Weekday (YC W21) is seeking a hands-on Lead Machine Learning Engineer to design, build, and scale production-grade Generative AI applications. The role focuses on AI assistants, retrieval and reasoning systems, agentic workflows, document intelligence, and intelligent automation capabilities that improve productivity, service quality, customer experience, and business outcomes.

You will work with Product, Engineering, Design, Security, Compliance, Operations, and business stakeholders to deliver

Qualifications

• 8+ years building applied AI/ML systems.
• 2+ years working with GenAI apps.
• At least one production GenAI app deployed to users.
• Experience taking GenAI to production beyond PoC.
• Ownership of production quality, reliability, and cost.
• Designing LLM apps using RAG, agentic workflows, tool use, grounding, and orchestration.

Responsibilities

• Design, build, and launch GenAI-powered applications.
• Identify high-impact AI opportunities across the business.
• Lead architecture, model selection, prompt engineering, retrieval, and data pipelines.
• Mentor engineers and drive production-ready AI standards.

Skills

Machine Learning

Generative AI

Production AI/ML Systems

LLM Applications

Python

AI Application Architecture

End-to-End Production AI

RAG

Agentic AI

Tool use

LLMOps

LangGraph

LangChain

LlamaIndex

PEFT / LoRA / QLoRA

Knowledge grounding

Tools

Cursor

Claude Code

LangGraph

LangChain

LlamaIndex

Job description

This role is for one of our clients
Industry: Software Development
Seniority level: Mid-Senior level
Min Experience: 9+ years
Location: Bengaluru
JobType: full-time
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
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