Machine Learning & LLM Engineer

Job description

Location New Delhi **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.