AI Solutions Engineer

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 AI Solutions Engineer, you'll bring the platform's AI capabilities to life. You'll integrate synthetic data generation into the pipeline, stand up and tune the annotation toolchain, and orchestrate reproducible ML workflows that the rest of the team can build on. You'll partner with the Solution Architect and Data/Annotation Engineer to turn raw corpora into high-quality, model-ready data. This role suits an engineer who's fluent across modern AI tooling and enjoys making sophisticated ML infrastructure actually work in production.

Key Responsibilities:

Configure and validate native AI-assistive features across bundled platform components (Dataset Explorer, DataCard Service, Annotation Platform)

Integrate and tune SAM 2 for full-motion video annotation: object tracking, segmentation calibration, confidence threshold configuration

Implement Frontier model API integration for synthetic data fidelity validation: prompt engineering, response validation, quality scoring

Configure AI-assisted annotation features: confidence scoring, auto-escalation triggers, model-assisted label suggestion

Implement ICAM / OIDC authentication integration with AFS identity framework

Configure data-layer DLP policies above the AFS-managed DLP infrastructure substrate

Configure NiFi FMV codec validation layer (H.264, H.265, MPEG-4) above AFS-managed substrate

Validate AI feature integration end-to-end across storefront, annotation platform, and DataCard write-back during Phase C

Must-Have Qualifications:

Bachelor's degree in Computer Science, Machine Learning, Data Science, or related field required; Master's degree preferred. Equivalent experience may substitute for degree on a 2-for-1 basis.

6+ years total professional experience, 4+ years hands-on AI/ML engineering

SAM 2 or equivalent foundation model integration for computer vision or video annotation

Frontier model API integration (OpenAI, Anthropic, or equivalent): async job management, quality validation pipelines

Python — strong, production-grade; comfortable with ML tooling and data pipeline development

Experience configuring AI-assistive features in annotation platforms or ML data tooling

Active Secret clearance with TS/SCI eligibility

Nice-to-Have Qualifications:

CVAT annotation platform — AI feature configuration and operation

DoD or IC data program experience: CUI, distribution statements, federal data governance

Evaluation design for AI/ML training data: IAA methodology, drift detection, model performance measurement

Video understanding or FMV annotation experience

DataCard or ML data provenance framework familiarity

The expected hourly salary range for this position is $75 to $80 p/hour, based on experience, skills, and qualifications.

Note to Candidates:

This role does not own infrastructure deployment. The AI Solutions Engineer operates at the AI/ML configuration and integration layer above the infrastructure. Ideal candidate is equally comfortable writing Python integration code and reasoning about model quality — and understands that in a federal data environment, every AI decision needs an audit trail.

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