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.
Responsibilities
- 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.
Requirements
- 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 NumPy) 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 PyTorch, TensorFlow, 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.
Additional Qualifications
- 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.
Travel Requirements
Remote position with occasional travel.
Technologies: Azure, Data Lake Storage, Azure SQL, Azure Data Factory, Microsoft Fabric, Foundry, Python, Pandas, NumPy, SQL, AWS, Google Cloud, PyTorch, TensorFlow, 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
