Solution Architect

Solution Architect

Hybrid - Washington D.C

Innodata is a global data engineering company. We believe that data and Artificial Intelligence (AI) are inextricably linked. Our mission is to enable the responsible advancement of artificial intelligence by providing the data, evaluation frameworks, and human expertise required to build AI systems that can be trusted at scale. We provide a range of transferable solutions, platforms, and services for Generative AI / AI builders and adopters. In every relationship, we honor our 36+ year legacy delivering the highest quality data and outstanding outcomes for our customers.

About the Program:

Innodata's Federal Practice builds the trusted data layer for critical infrastructure Trust & Safety work. Partnering with a leading systems integrator, we're delivering a modern, governed data services platform in a secure federal (IL4) environment. Over an intensive 20-week phase, you'll help stand up a data services storefront, a DataCard governance framework, synthetic data integration, and Databricks write-back capabilities.

About the Role:

As the Solution Architect, you'll design the backbone of the platform. You'll define the architecture for the data services storefront and own the DataCard governance framework that keeps our data trustworthy, documented, and compliant end to end. Working closely with the engineering team and the Delivery Owner, you'll translate governance and security requirements in a secure federal (IL4) environment into a clean, defensible technical design. It's a role for an architect who cares as much about data stewardship and governance as about elegant systems.

Key Responsibilities:

• Own cross-pillar architectural integrity and interface coordination across the 24-week program
• Design and stand up the DataCard governance framework: schema design, distribution-statement methodology (DoD Dir. 5230.24), provenance lineage
• Deploy and configure ZenML multi-cloud orchestration stack per Innodata Layer 2 Multi-Cloud Architecture v3
• Design taxonomy engine and sequestration controls architecture
• Confirm NIST SP 800-53 Rev 5 control split between Innodata data-layer and AFS infrastructure-layer scope
• Lead platform component run books and per-DataCard documentation in Phase E
• Coordinate with AI Solutions Engineer on Kubernetes-adjacent data-layer DLP policy configuration
• Coordinate with Backend Engineer on Databricks write-back schema and DataCard write-back path

Must-Have Qualifications:

• Bachelor's degree in Computer Science, Data Engineering, or related field required; Master's degree preferred. Equivalent experience may substitute for degree on a 2-for-1 basis.
• 10+ years total professional experience, 6+ years in data architecture or platform engineering
• Databricks, Delta Lake, and MLflow — hands-on implementation experience, not conceptual
• Data governance frameworks: DataCards, provenance, lineage, distribution statement controls
• ZenML or equivalent ML orchestration tooling (Airflow or Prefect acceptable; must be willing to ramp ZenML quickly)
• Working knowledge of IL4/IL5 boundary concepts, NIST SP 800-53 Rev 5, and DoD data classification requirements
• Active Secret clearance with TS/SCI eligibility

Nice-to-Have Qualifications:

• Prior Innodata or annotation platform architecture experience
• MLflow model governance and registry hands-on experience
• Ontology and taxonomy design for AI/ML training datasets
• DoD Directive 5230.24 distribution statement implementation experience

The expected hourly salary range for this position is $70 to $75 p/hour, based on experience, skills, and qualifications.