AI Solutions Engineer: Production ML for Federal Data

Innodata Inc.; Washington; On-site; USD 103,000 - 110,000; Full time

Job summary

Innodata Inc. is seeking an AI Solutions Engineer to bring the platform’s AI capabilities to life. You will integrate synthetic data generation, tune annotation toolchains, and orchestrate reproducible ML workflows in a federal data environment, collaborating with architects and data/annotation engineers to produce model-ready data.

The role emphasizes production-grade Python development, AI tooling, and ensuring audit trails for all AI decisions within secured infrastructures.

Qualifications

• Bachelor’s degree in CS/ML/Data Science; equivalent experience may substitute.
• 6+ years total experience; 4+ years AI/ML engineering.
• Experience with SAM 2 or similar foundation model integration for computer vision.
• Frontier model API integration (OpenAI, Anthropic, or equivalent).
• Python — production-grade; ML data pipelines.
• Active Secret clearance with TS/SCI eligibility.

Responsibilities

• Configure and validate AI features across platform components (Dataset Explorer, DataCard Service, Annotation Platform).
• Integrate and tune SAM 2 for video annotation: object tracking and segmentation calibration.
• Implement Frontier model API integration for synthetic data fidelity validation: prompt engineering and quality scoring.
• Configure AI-assisted annotation features: confidence scoring and auto-escalation triggers.
• Configure authentication/integration layers (if applicable) and data governance touches.

Skills

Python

Production-grade ML

SAM 2 integration

Frontier model APIs

Clearance TS/SCI eligibility

AI/ML tooling

Annotation platforms

Data pipelines

Education

Bachelor's degree in CS/ML/Data Science

Master's degree preferred

Equivalent experience 2-for-1 basis

Tools

CVAT annotation platform

Job description

Innodata Inc. is seeking an AI Solutions Engineer to bring the platform’s AI capabilities to life. You will integrate synthetic data generation, tune annotation toolchains, and orchestrate reproducible ML workflows in a federal data environment, collaborating with architects and data/annotation engineers to produce model-ready data.

The role emphasizes production-grade Python development, AI tooling, and ensuring audit trails for all AI decisions within secured infrastructures.