Lead Machine Learning Engineer

This role is for one of our clients
 
Industry: Marketing Services
Seniority level: Mid-Senior level
 
Experience: 4+ yrs
Location: Mumbai, Maharashtra, India
Job Type: 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.
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