AI / Data Engineer: Machine Learning, Data Platforms & Applied AI

Intertwine Associates•Mount Pleasant, SC

2d•$90,000 - $220,000•Remote

About The Position

Work in a dynamic, fast-paced environment as an AI / Data Engineer supporting federal agencies and other clients. Design, build, and operate the data platforms, machine learning models, and AI applications that help clients turn their data into decisions, from the first pipeline to production systems used every day. Collaborate with client program managers, data scientists, analysts, architects, and governance teams, and bring practical engineering judgment to missions across health, science, security, and public service. Build production-grade data pipelines that ingest, transform, and validate structured and unstructured data, with schema enforcement, data quality checks, and lineage. Develop, evaluate, and deploy machine learning and AI solutions, including applications built on large language models such as retrieval-augmented generation, document understanding, and agentic workflows. Package models and data as secure APIs and services that other systems can rely on, and implement the logging, monitoring, testing, and performance tuning that keep them reliable. Work in cloud environments (Microsoft Azure, AWS, or Google Cloud) and apply security, privacy, and responsible AI practices suited to regulated and government settings. Strong client or customer engagement is required in this role. You will interface directly with client stakeholders and are expected to represent Intertwine Associates and the client with professionalism, responsiveness, and sound judgment at every touchpoint. This is a remote position with occasional travel. Assignments are matched to client needs and to each candidate's expertise and eligibility.

Requirements

Bachelor's degree in computer science, data science, statistics, engineering, or a related field, or equivalent professional experience.
3+ years of experience building data pipelines, machine learning models, or AI applications that run in production.
Proficiency in Python and its data libraries (e.g., pandas and NumPy).
Strong SQL skills.
Hands-on experience with at least one major cloud platform (Microsoft Azure, AWS, or Google Cloud).
Experience with machine learning frameworks such as scikit-learn, PyTorch, or TensorFlow.
Experience with data validation, testing, and quality assurance.
Experience building APIs or services that expose data or models to other applications.
Excellent written and verbal communication skills, including explaining technical work to non-technical audiences.
Ability to work independently and collaboratively as part of a team, and to prioritize work.
U.S. citizenship or lawful permanent residency may be required per contract.
Ability to obtain a federal government Public Trust or security clearance where the assignment requires one.

Nice To Haves

Experience building applications on large language models: retrieval-augmented generation, embeddings and vector databases, evaluation, and guardrails.
MLOps experience: experiment tracking, model registries, CI/CD, containers, and orchestration (e.g., MLflow, Docker, Kubernetes, Airflow).
Experience with modern data platforms such as Databricks, Snowflake, Microsoft Fabric, Azure Data Factory, or AWS Glue.
Cloud or data certifications (e.g., Microsoft Azure Data Engineer or AI Engineer, AWS Machine Learning, Google Professional Data Engineer).
Familiarity with federal security and AI governance frameworks (e.g., FedRAMP, FISMA, NIST AI Risk Management Framework).
Experience with health, scientific, or research data, or with supporting a federal agency.
An active federal Public Trust or security clearance.

Responsibilities

Design, build, and operate data platforms, machine learning models, and AI applications.
Build production-grade data pipelines that ingest, transform, and validate structured and unstructured data.
Develop, evaluate, and deploy machine learning and AI solutions, including applications built on large language models.
Package models and data as secure APIs and services.
Implement logging, monitoring, testing, and performance tuning for AI/ML systems.
Work in cloud environments (Microsoft Azure, AWS, or Google Cloud).
Apply security, privacy, and responsible AI practices.
Interface directly with client stakeholders.
Represent Intertwine Associates and the client with professionalism, responsiveness, and sound judgment.