Machine Learning & LLM Engineer

• 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.