Job description

Innodata (Nasdaq: INOD) 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. Note to Candidates: This role is the primary technical decision-maker on the program below the Delivery Owner. The SA is expected to make architectural calls independently within the established framework and surface only unresolved cross-cutting decisions to the Delivery Owner. Please be aware of recruitment scams involving individuals or organizations falsely claiming to represent employers. Innodata will never ask for payment, banking details, or sensitive personal information during the application process. To learn more on how to recognize job scams, please visit the Federal Trade Commission’s guide at consumer.ftc.gov. If you believe you’ve been targeted by a recruitment scam, please report it to Innodata at verifyjoboffer@innodata.com and consider reporting it to the FTC at ReportFraud.ftc.gov .

Requirements

Must-haves

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
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
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
If you believe you’ve been targeted by a recruitment scam, please report it to Innodata at verifyjoboffer@innodata.com and consider reporting it to the FTC at ReportFraud.ftc.gov .

Nice-to-haves

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.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aikubernetesml

Platform: greenhouse
Effort: quick