AI Solutions Engineer
innodata • Hybrid - Washington D.C
Education
Bachelor
Type
Hourly
Pay Rate
$75–$80/hr
Listed
86d ago
About this role
From the innodata listing
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
Requirements
- Must be eligible to work in Hybrid - Washington D.C
- Fluent proficiency in English (Written & Verbal)
- Reliable high-speed internet connection
- Bachelor's degree or equivalent professional experience
- Demonstrated expertise in STEM
Interview Prep
Sample questions for a AI Engineer role, written in-house to help you prepare.
How do you decide which evaluation metric to optimize for a classification model when the classes are imbalanced?
Accuracy is misleading on imbalanced data because a model can score well by always predicting the majority class. I look at precision, recall, and F1 for the minority class specifically, and pick the metric that matches the real cost of false positives versus false negatives in the deployment context, since those costs are rarely symmetric in practice.
What is your process for debugging a deep learning model that trains fine but performs poorly at inference time?
I start by checking for a train/inference mismatch: different preprocessing, batch normalization behaving differently in eval mode, or data leakage during training that inflated validation scores. Comparing the exact input pipeline used at training time against the one used at inference usually surfaces the discrepancy before I need to touch the model architecture itself.
How do you approach feature selection when working with high-dimensional tabular data?
I start with correlation and mutual information analysis to drop obviously redundant features, then use a tree-based model's feature importance as a first pass filter. From there, recursive feature elimination with cross-validation tells me where the marginal value of additional features drops off, which keeps the final feature set both smaller and more robust to overfitting.
Explain how you would detect and address data drift in a production machine learning pipeline.
I monitor the statistical distribution of input features and model outputs over time, using something like population stability index or KL divergence against a reference window. When drift crosses a threshold, I retrain on recent data rather than the full historical set, and I keep an alert in place so drift is caught before it shows up as a quality regression.
This listing calls for these tools directly. Prep for the technical screen:
Why this role
This AI Solutions Engineer opening pays $75–$80/hr and draws on STEM knowledge you've already built. Onboarding covers the AI training process itself.
Talent pool
We're light on STEM candidates
We've matched 65 people with a STEM background against 726 STEM listings we've tracked, so most go out without one. Set up a profile and we'll consider you for a role like this one.
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Common questions
Is academic-niche AI training just data labeling?
No, it's closer to academic research. Expect to write or verify complex proofs, solve advanced equations, or check the logic behind a model's step-by-step reasoning. The goal is teaching AI systems to reason deeply within your specific field.
Do I need a PhD for academic-niche AI training roles?
For the top pay tiers, a PhD or current enrollment is usually expected. But the domain assessment is what decides it: if you can solve the problems, the degree becomes secondary.
What does STEM work look like for an AI Solutions Engineer?
Tasks here are scoped to STEM, not generic labeling. As an AI Solutions Engineer, expect to draw on real domain judgment (evaluating outputs, correcting errors, or providing expert reasoning specific to STEM) rather than following a one-size-fits-all rubric. If you don't have hands-on STEM background, this is likely not the right listing to start with.
What specific skills does this listing call for?
Coding, Python, and TypeScript are named directly in the listing. If you don't have hands-on experience with these, expect the screening process to test for them directly rather than accepting adjacent experience as a substitute.
How much does this specific role pay?
This listing is posted at $75–$80/hr, an hourly rate. The range reflects experience level and negotiated terms, not a placeholder, so where you land in it depends on your background and the assessment. Pay can change between when we last checked the listing and when you apply, so confirm the current number on the platform's own application page before committing time.
Can I apply from outside Hybrid - Washington D.C?
This specific role is open only to people based in Hybrid - Washington D.C. If you are somewhere else, applying is unlikely to lead to an offer even if you pass the assessment, because the restriction is usually about where the work can legally be contracted rather than your skills. Read the full description for any tax-residency or right-to-work caveats before you apply.