Applied AI Engineer

You will be working with the Head of Data and AI office on R&D and Product initiatives on every stage of an AI and Data lifecycle within our Montrose Software AI and Data framework. With empowered product team principles you will assist defining MVPs tailored to the needs of clients, understand the data intimately to surface the insights that matter to clients, developing proof of concepts in notebooks, and productionizing the MVPs with the MLOps and LLMOps mindset. Beneficial experience includes data engineering within cloud-native architectures for executing data, AI, and enterprise systems at scale. Prior experience delivering on Lakehouse platforms- i.e. Databricks in particular- is a strong advantage.

• Define and develop proof of concepts for Data and AI products and solutions
• Conduct market and technology research; architect optimal solutions to address business challenges
• Map AI/ML techniques to specific business problems
• Build and productionise solutions on the Lakehouse (Databricks: Unity Catalog, Delta Lake, MLflow, Model Serving, Mosaic AI Agent Framework, Vector Search)
• Forecast operational AI costs for solutions at scale
• Support business intelligence reporting to guide Data solutions
• Develop end-to-end data products and solutions with an MLOps and LLMOps mindset within Montrose Software’s Data and AI framework
• Conduct product and feature discovery meetings with Clients aiming for a cost efficient solution addressing clients goals within their context like budgets, operations, talent pools, data maturity.
• Execute within Montrose's AI delivery and ops patterns and best practices.

• 3+ years of experience delivering end-to-end Data and AI solutions and products
• Experience in delivering systems and solutions with LLM agentic architectures
• Understanding of cloud-native architectures and operations
• Hands-on experience with Databricks or a comparable Lakehouse platform (Snowflake, BigQuery + Vertex, Microsoft Fabric)
• Familiarity with machine learning mathematical toolboxes
• Experience with containerized, scalable, elastic deployments, microservice architecture (REST, gRPC)
• Comfortable with data engineering
• Proficiency with SQL
• Understanding of data architectures: OLTP, OLAP, vector and graph databases, column-based file formats
• Knowledge of DevOps, MLOps, LLMOps best practices
• Innovative, curious, outcome-focused mindset
• Collaborative mindset striving for win–win situations
• Strong analytical and systems thinking
• Experience in Transformers, PyTorch, Jupyter notebooks, Python, pandas, NumPy, Matplotlib, scikit-learn

• Databricks certifications (Data Engineer Associate/Professional, ML Associate/Professional, Generative AI Engineer Associate)

• Technical phone screening (~30 minutes)
• Technical interview(~2 hours)
• Non-technical call with hiring manager or one of the Recruitment Team(~45 min)