Overview In this role you lead the design and deployment of scalable ML solutions for clients in enterprise settings. You will work with cross-functional teams to translate business needs into data-driven models and production-ready systems. You help grow CapTech's ML/Datascience practice through client engagements and thought leadership. The role combines technical leadership with hands-on development, focusing on impactful, validated AI solutions that scale. You'll operate at the intersection of strategy, engineering, and client outcomes, delivering measurable value.
Compensation / Benefits- learning & development programs
- modern health platform
- fertility and family-forming coverage
- fringe benefits stipend
- 401(k) matching
- flexible work arrangements
Responsibilities- Strategize with clients and cross-functional teams to deliver end-to-end ML solutions and explore new approaches
- Provide technical leadership ensuring alignment with customer needs across teams
- Deconstruct client needs into data-driven processes and analytical measures
- Analyze and transform large datasets on enterprise platforms (AWS, Azure, GCP)
- Design, develop, and deploy advanced analytical solutions (recommender systems, NLP, risk scoring)
- Productionize ML systems with optimization and scalability in mind
- Grow ML/Datascience practices via client presentations, proposals, and business development, leading junior data scientists and engineers
Key requirements- 7+ years delivering data engineering and ML solutions on cloud platforms
- Bachelor's degree or equivalent combination of education and experience
- Experience providing technical leadership and mentoring engineers in data engineering
- Hands-on experience with large datasets (multi-billion records)
- Proficiency in Python, Scala, or similar languages
- SQL, Spark, NoSQL, and cloud data processing in production
- Docker and microservices experience
- Experience with data warehousing tools (Snowflake, Databricks, Azure SQL, Amazon RDS)
- Production-scale ML systems experience across domains (personalization, NLP, CV)
- Knowledge of DevOps, statistics, model management/versioning, and LLMs in production
- Experience with prompt engineering, MCP and RAG, and agentic AI architectures
- Experience with LangChain, n8n, pydantic and multi-agent orchestration
- Technical leadership and mentoring
- Cross-functional collaboration
- Client communication and presentation
- Python
- Scala
- SQL
