Machine Learning / Data Science Engineer

CapTech is an award-winning consulting firm that collaborates with clients to achieve what’s possible through the power of technology. At CapTech, we’re passionate about the work we do and the results we achieve for our clients. From the outset, our founders shared a collective passion to create a consultancy centered on strong relationships that would stand the test of time. Today we work alongside clients that include Fortune 100 companies, mid-sized enterprises, and government agencies, a list that spans across the country.

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.

Responsibilities

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.

Requirements

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; Strong understanding of conversational UX and prompt evaluation metrics; Experience with agentic frameworks in practice (langchain, n8n, pydantic, etc.); Experience with multi-agent orchestration