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

Intertwine Associates partners with federal agencies and other clients to deliver modern data and AI systems. In this remote role based in Mount Pleasant, SC
, you will lead the design and delivery of Azure-based data platforms
, machine learning
, and applied AI applications
, while setting engineering standards and working closely with client teams.

This position offers an anticipated base salary range of $130,000–$175,000 per year
. You may also be eligible for discretionary bonuses, signing or retention incentives, or other variable compensation
, which are not guaranteed
.

• Architect and lead enterprise data platforms and pipelines on Azure
  , including Data Lake Storage
  , Azure SQL
  , Data Factory
  , and Microsoft Fabric / Foundry (or equivalent cloud services).
• Define standards for ETL design
  , schema enforcement
  , data validation
  , quality checks
  , and lineage
  , and review other engineers’ work against those expectations.
• Design, build, and deploy machine learning and AI solutions
  , including applications using large language models such as retrieval-augmented generation
  , document understanding
  , and agentic workflows
  , with evaluation and guardrails
  .
• Build secure API endpoints and microservices to make data and models available to analytics and ML platforms
  .
• Implement MLOps practices covering experiment tracking
  , model registries
  , CI/CD
  , containers
  , monitoring
  , and cost and performance tuning
  .
• Apply data and AI governance for compliance
  , reproducibility
  , auditability
  , and responsible AI
  , including federal frameworks such as the NIST AI Risk Management Framework
  .
• Translate mission and business needs into technical roadmaps
  , estimates
  , and briefings for client leadership
  .
• Mentor engineers and data scientists and lead technical work across multidisciplinary teams.

• 7+ years of experience building data pipelines, machine learning systems, or AI applications in production, including 2+ years leading technical work.
• Expert Python (including Pandas and Num Py
  ) and advanced SQL
  , including experience integrating relational and unstructured sources
  .
• Deep hands-on experience with Azure data services
  , or equivalent depth with AWS or Google Cloud
  .
• Production experience with machine learning frameworks such as Py Torch ,
  Tensor Flow
  , or scikit-learn
  .
• Experience with data validation
  , schema enforcement
  , quality assurance
  , and governance in regulated environments
  .
• Experience designing APIs and microservices for analytics and ML platforms
  .
• Excellent written and verbal communication, including the ability to brief senior, non-technical audiences
  .
• U.S. citizenship or lawful permanent residency may be required per contract.
• Ability to obtain a federal government Public Trust or security clearance where required by the assignment.

• Bachelor’s or advanced degree in Computer Science
  , Data Science
  , Statistics
  , Engineering
  , or a related field, or equivalent professional experience.
• Experience building applications on large language models, including retrieval-augmented generation
  , embeddings and vector databases
  , evaluation
  , and guardrails
  .
• Microsoft certifications such as Azure Data Engineer
  , Azure AI Engineer
  , Fabric Data Engineer
  , or Azure Solutions Architect
  , or equivalent AWS or Google Cloud certifications.
• Experience with Databricks
  , Snowflake
  , Microsoft Fabric
  , or similar modern data platforms at enterprise scale.
• Familiarity with FedRAMP and FISMA and federal data security requirements.
• Experience supporting a federal agency, or health
  , scientific
  , or research data
  .
• An active federal Public Trust or security clearance
  .

Remote position with occasional travel
.

Technologies: Azure, Data Lake Storage, Azure SQL, Azure Data Factory, Microsoft Fabric, Foundry, Python, Pandas, Num Py, SQL, AWS, Google Cloud, PyTorch, Tensor Flow, scikit-learn, retrieval-augmented generation, document understanding, agentic workflows, large language models, APIs, microservices, MLOps, CI/CD, containers, NIST AI Risk Management Framework, Databricks, Snowflake, FedRAMP, FISMA