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Job Description Summary
This position has a wide range of responsibilities that includes both data and AI engineering. They will be responsible for designing and implementing data infrastructure to extract, clean, move and store data. They will need the ability to independently develop AI/ML systems and products for both internal and external use. They will communicate with business stakeholders to understand their needs and develop solutions to address them.
Job Description
Scope of Position
This position has a wide range of responsibilities that includes both data and AI engineering. They will be responsible for designing and implementing data infrastructure to extract, clean, move and store data. They will need the ability to independently develop AI/ML systems and products for both internal and external use. They will communicate with business stakeholders to understand their needs and develop solutions to address them.
Essential Job Duties
Communication: Strong communication skills to learn and collaborate with stakeholders across the organization.
Independent Problem-Solving: Strong, independent analytical and problem-solving abilities, and the internal drive to execute projects to completion.
Project Management: Ability to manage projects, prioritize tasks, and meet deadlines.
Adaptability: Willingness to learn and adapt to changing business needs, requirements, and emerging technologies.
AI/ML Engineering
Machine Learning & AI Systems: Strong Python skills for building, training, and deploying both traditional ML models and modern AI applications — including LLM-based systems, RAG pipelines, and agentic workflows. Proficiency in feature extraction/transformation and model selection, training, and evaluation.
GenAI Tooling: Experience with LangChain and LangGraph for building agentic/AI workflows, and working with LLM APIs such as the Claude and OpenAI SDKs. Experience self-hosting and serving models with vLLM is a plus.
API Development: Proficiency building and serving APIs with FastAPI, using Pydantic for data validation and schema enforcement.
Statistical & Mathematical Rigor: Solid grounding in statistical methods and experimental design (e.g., hypothesis testing, regression, causal inference) to validate models and ensure sound decision-making.
MLOps/LLMOps: Experience deploying, monitoring, and maintaining models and AI systems in production, using tools such as MLflow (experiment tracking) and LangSmith/LangFuse (LLM tracing and evaluation).
Data Engineering
Data Modeling: Discover and characterize source data systems, understand and model the underlying business concepts, and build data models that organize data to meet operational and reporting needs.
Database Development & Optimization: Proficiency with databases (T-SQL, NoSQL) — writing and optimizing tables, queries, and indexes for scalability, reliability, and performance.
ETL/ELT Pipelines: Design and implement pipelines to move and transform data between systems.
Big Data / Data Warehousing: Experience with data warehousing concepts and platforms like Databricks; familiarity with Spark and Python for large-scale data processing.
Cloud Platforms: Knowledge of cloud services, particularly Azure, for scalable data storage and processing.
Data Governance & Security: Awareness of data quality, privacy, security, and compliance best practices.
Education & Qualification Requirements
Degree in Computer Science, Physics, Mathematics, or a similar field; Master's degree a plus.
3–5 years of experience as a data engineer, ML engineer, AI engineer, AI infrastructure engineer, or in a similar role.