Lead Machine Learning Engineer — Enterprise AI Solutions

National Science Teachers Association; Reston, Northern (VA, KY); Hybrid; USD 90,000 - 200,000; Full time

Benefits offered by this job

Flexible work environment

Learning & Development

Health coverage

401(k) matching

Job summary

CapTech is seeking a Lead Machine Learning Engineer to design and deploy scalable ML systems for enterprise clients. You will collaborate with data scientists, engineers, and client teams to deliver end-to-end data-driven solutions.

You will provide technical leadership, decompose client needs into data workflows, and optimize models on platforms like AWS, Azure, and GCP. The role emphasizes production-ready ML, experimentation, and scaling, with opportunities to mentor juniors and grow the

Qualifications

• 7+ years delivering data engineering and machine learning solutions on cloud platforms.
• Experience providing technical leadership and mentoring other engineers in data engineering space
• 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

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
• Provide technical leadership and collaborate within and across teams to ensure that the overall technical solution is aligned with the customer 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.

Skills

Python

Scala

LLMs

Prompt engineering

Multi-agent AI

Data engineering

Machine learning

Education

Bachelor's degree or equivalent

Tools

Docker

Snowflake

Databricks

Azure SQL

Amazon RDS

Spark

NoSQL

LangChain

n8n

pydantic

Job description

CapTech is seeking a Lead Machine Learning Engineer to design and deploy scalable ML systems for enterprise clients. You will collaborate with data scientists, engineers, and client teams to deliver end-to-end data-driven solutions.

You will provide technical leadership, decompose client needs into data workflows, and optimize models on platforms like AWS, Azure, and GCP. The role emphasizes production-ready ML, experimentation, and scaling, with opportunities to mentor juniors and grow the