Principal Data & ML Ops Architect; Remote, Continental

About ICA, Inc.

ICA, Inc. is a rapidly growing company, located in the D.C./Metro area. We were founded in 2009 to assist government clients with evaluating and achieving their objectives. We have become a trusted advisor helping our clients by offering cutting‑edge innovation and solutions to complex projects. Our small company has grown significantly, and we're overjoyed at the opportunity to expand yet again!

We are results-focused and have a proven track record supporting federal agencies and large government services primes in three main areas:
Research and Data Analysis, Advanced-Data Science, and Strategic Services. We currently support multiple analytics and research programs across HHS.

At ICA, we believe our success starts with our people. We foster a collaborative "one team" environment where work-life balance isn't just talked about – it's prioritized. We're building dynamic, highly skilled teams in a welcoming and supportive atmosphere. If you're passionate about using your technical expertise to make a difference, we want to talk to you.

We are seeking a hands‑on Principal Data & ML Ops Architect to lead the architecture of our data and document‑intelligence platforms. ICA works across healthcare, federal health, regulatory and life‑sciences environments, building systems that turn complex structured and unstructured data into reliable, actionable intelligence.

This role is responsible for designing scalable platforms that ingest, process, govern, and serve large volumes of data and documents for analytics, AI/ML, RAG, search, and operational applications. You will help modernize and unify existing platforms while establishing reusable architectural patterns for new solutions.

• Architect large-scale data and document-processing platforms, from ingestion through downstream consumption.
• Design robust patterns for document parsing, extraction, normalization, validation, storage, search, human review, and reprocessing.
• Establish standards for schemas, metadata, lineage, provenance, data quality, reproducibility, and auditability.
• Design reliable ingestion and transformation pipelines using APIs, files, batch processing, and streaming where appropriate.
• Create platform patterns that enable Data Science and AI prototypes to become secure, maintainable production solutions.
• Architect data and retrieval foundations supporting RAG, analytics, AI workflows, evaluation, and feedback loops.
• Make architecture decisions involving scalability, reliability, security, privacy, cost, and operational complexity.
• Lead technical design reviews, investigate production failures, mentor engineers, and work across Data Engineering, Data Science, Dev Ops/MLOps, Product, Security, and client teams.
• Remain technically hands‑on enough to review and develop Python, SQL, schemas, APIs, and production data pipelines.

• Deep experience architecting and owning production data platforms or distributed data systems.
• Strong Python, SQL, data modeling, ETL/ELT, workflow orchestration, and cloud‑data‑platform experience.
• Significant experience with unstructured data or document‑processing systems at scale.
• Strong understanding of metadata, schema evolution, lineage, provenance, data quality, observability, retries, replay, backfills, and recovery.
• Experience designing systems with sensitive‑data controls, governance, access management, and audit requirements.
• Strong understanding of how modern AI/ML and retrieval systems depend on production data architecture.
• Demonstrated technical judgment, ownership, problem solving, and ability to challenge fragile or overly complex designs.
• Strong technical communication and decision‑making skills.
• Must be authorized to work in the…