Machine Learning / Data Science Engineer

CapTech Machine Learning Engineers are responsible for designing and implementing data-driven solutions for our clients, with a specific focus on building and deploying scalable machine learning systems in enterprise environments. CapTech employees enjoy a collaborative environment and have many opportunities to learn from and share knowledge with other CapTech analysts, architects, and our clients.

Specific responsibilities for the Machine Learning Engineer position include:

• Strategizing with clients, data scientists, engineers, and other members of cross-functional teams to implement end-to-end machine learning solutions and identify new machine learning and data science approaches to meet business needs
• Deconstructing client needs into data-driven processes/models and analytical measures.
• Analyzing and transforming large datasets hosted on a variety of enterprise-level data platforms (e.g., AWS, Azure, GCP).
• Designing, developing, and deploying advanced analytical solutions leveraging client data (e.g., recommender systems, natural language processing, risk scoring).
• Productionizing ML systems with a focus on optimization and scalability to satisfy clients’ requirements.
• Growing CapTech’s Machine Learning and Data Science practices through delivering client presentations, writing proposals, attending various business development events, and leading teams of junior data scientists and engineers.

• Bachelor's degree or equivalent combination of education and experience.
• Hands-on experience manipulating and analyzing large (multi-billion record) data sets.
• Hands-on experience developing data-driven solutions using Python, Scala, or similar languages.
• Proficiency leveraging SQL, Spark, NoSQL, and/or cloud data processing frameworks in a production setting.
• Proficiency with containerization (e.g., Docker) and microservices.
• Proficiency with data warehousing tools/environments such as Snowflake, Databricks, Azure SQL, Amazon RDS
• Comfort and proficiency in framing data-driven problems from cross-industry business requirements.
• Experience applying analytical methods across multiple business domains (e.g., customer analytics, marketing, finance, digital channels)
• Hands-on experience implementing production-scale machine learning systems in one or more domains (i.e., personalization, natural language processing, computer vision).
• Knowledge of DevOps and automation best practices.
• Knowledge of statistics and statistical modeling methods.
• Knowledge of model management and model versioning best practices.
• Experience working with LLMs (e.g., GPT, Claude, Mistral, etc.) in production setting
• Experience with prompt engineering, MCP and RAG, and agentic AI architectures