Machine Learning & LLM Engineer
L.E.K. Consulting
New Delhi
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Job Description
Mid-Level ML & LLM Engineer
Location: Gurgaon
Employment Type: Full-time
Experience: 2–5 years
About the Role
We are building a Data and AI Enablement team and are looking for a motivated mid-level ML & LLM Engineer to design, deploy, and maintain AI-driven data solutions.
In this role, you will work across the full stack, from data pipelines and lakehouse architectures to LLM-powered applications. You will collaborate closely with stakeholders to build scalable solutions, surface actionable insights, and support the development of modern AI and data capabilities on cloud infrastructure.
What You’ll Do
Design and deploy traditional machine learning and LLM-based solutions to solve real business problems.
Build and maintain data pipelines, lakehouse architectures using Microsoft Fabric, and RAG systems using tools such as Azure Search Indexes and ChromaDB.
Grade, refine, and enrich datasets to improve ML and LLM model training, including use of medallion architecture and synthetic data principles.
Develop and ship web applications and dashboards on Azure, including KPI visualization and user-focused interfaces.
Maintain high code quality through Git-based workflows, code reviews, documentation, and DevOps best practices.
Partner with cross-functional teams to translate business requirements into scalable, production-ready technical solutions.
Communicate AI capabilities, limitations, risks, and trade-offs clearly to both technical and non-technical stakeholders.
Required Skills and Experience
AI & Machine Learning
Strong understanding of large language models, including capabilities, limitations, and responsible use.
Experience with prompt engineering and AI output evaluation.
Knowledge of traditional machine learning techniques such as classification, NLP, and sentiment analysis.
Experience with retrieval-augmented generation, or RAG.
Understanding of LLM-related concepts such as hallucination, token usage, quantization, context window limitations, and model reliability.
Familiarity with AI evaluation frameworks and responsible AI deployment practices.
AI Capabilities, Limitations & Agentic AI
Ability to assess when AI tools are appropriate and when traditional methods may be more effective.
Understanding of common AI failure modes, including hallucination, bias, prompt injection, and context limitations.
Experience with Model Context Protocol, or MCP, including integrating AI agents with enterprise APIs, tools, and data sources.
Practical knowledge of agentic AI architectures and multi-step reasoning workflows.
Comfort working within responsible AI guidelines and explaining AI limitations to stakeholders.
Problem Solving & Communication
Structured and analytical approach to solving complex and ambiguous business problems.
Ability to translate stakeholder needs into scalable technical designs.
Experience evaluating trade-offs across data, model, and infrastructure choices.
Strong written and verbal communication skills with both technical and non-technical audiences.
Data & Databases
Strong SQL skills, including querying, modeling, and optimization.
Experience with data pipeline design and ETL.
Familiarity with lakehouse architecture, preferably Microsoft Fabric.
Experience with vector databases for RAG use cases.
Cloud & Infrastructure
Experience with Microsoft Azure.
Familiarity with Docker and containerization.
Experience with web application deployment and CI/CD workflows.
Experience with UX and KPI dashboarding is a plus.
Languages & DevOps
Strong Python skills.
Strong SQL skills.
Experience with Git and version control.
Ability to write clean, well-documented, maintainable code.
Education
A Bachelor’s degree in a technology-related field is required. Relevant disciplines include:
Computer Science
Data Science or Data Engineering
Software Engineering
Information Systems or Information Technology
Mathematics, Statistics, or a related quantitative field
Nice to Have
2+ years of experience in a consulting environment.
Experience with MLOps practices, including model monitoring, versioning, and deployment pipelines.
Familiarity with data governance and cloud security best practices.
Experience with orchestration tools such as Azure Data Factory.
UX design sensibility for dashboards and application interfaces.
What We Offer
A collaborative hybrid work environment with flexibility.
The opportunity to help build and shape a new Data & AI Enablement team from the ground up.
Exposure to cutting-edge AI and data technologies in a hands-on role.
Mentorship and growth pathways within a fast-moving data organization.
We are an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees.
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