AI/ML & Intelligent Automation Lead

Take AI/ML from demo to daily use on a federal health program. As the AI/ML Intelligent Automation Lead you will lead capabilities from data preparation through inference, evaluation and monitoring, building on the program’s FHIR and OMOP data pipelines. Integrating commercial AI/ML services counts, original model development is not required, and no generative-AI or AI-Native certification is needed. Working knowledge of SAFe is expected, but SAFe certification is not required.

Location: Reston, VA (onsite preferred). Primarily remote within the US, considered for highly qualified candidates, with frequent travel to Reston.

Clearance: US citizen able to obtain and maintain an ADP II clearance.

Remote candidates: you will be required to keep your camera on during the interview, and your interviewer will take your picture for identification purposes if an offer letter is extended to you.

This is in anticipation of future work!

What you'll do

• Lead delivery of AI/ML and automation capabilities into production and operational pilots.
• Build automated data preparation and integrate model-serving or inference APIs, including commercial services. Support low-latency clinical inference and natural language processing use cases, and use feature stores where appropriate.
• Define evaluations and testable acceptance criteria, and keep capabilities version-controlled and monitored in operation, and follow Government AI governance and PHI-handling requirements.
• Work with the architects on data contracts, and with cyber security on hosting, monitoring and security of AI services.
• Document models, data pipelines and evaluation results so the Government can sustain them. Deliver through iterative backlogs, demo results, and act on user and operational feedback in a SAFe delivery framework using the Government’s Azure DevOps.

What you bring

• Bachelor’s in computer science, data science, mathematics, statistics, engineering, health informatics or a related field with 8-12yrs. of prior relevant experience.
• 8+ years in software engineering, data engineering or applied analytics, including 3 implementing or integrating AI/ML. The 8 years need not all be in AI/ML.
• At least one AI/ML capability delivered into production or an operational pilot used by end users, covering automated data preparation, model-serving or inference-API integration, evaluation, version control and operational monitoring. Research-only or demo-only experience does not qualify. Experience with health or other regulated data is preferred.
• Experience building on cloud AI/ML services in AWS GovCloud or an equivalent government cloud environment, and with CI/CD and model-versioning (MLOps) practices.
• Agile delivery experience: iterative backlogs, testable acceptance criteria, results demos, and incorporating user and operational feedback , with working knowledge of SAFe and Azure DevOps (certification not required).
• Ability to meet the applicable Government work-role requirements: Ability to meet the applicable Government work-role requirements: Government designated training for HIPAA Compliance, Information Assurance and applicable background investigation.
• Knowledge of MHS/DHA environment , such as MHS GENESIS, the Joint Health Information Exchange, and DoD-VA data exchange.

If you're looking for comfort, keep scrolling. At Leidos, we outthink, outbuild, and outpace the status quo — because the mission demands it. We're not hiring followers. We're recruiting the ones who disrupt, provoke, and refuse to fail. Step 10 is ancient history. We're already at step 30 — and moving faster than anyone else dares.

Original Posting:

For U.S. Positions: While subject to change based on business needs, Leidos reasonably anticipates that this job requisition will remain open for at least 3 days with an anticipated close date of no earlier than 3 days after the original posting date as listed above.

Pay Range:

The Leidos pay range for this job level is a general guideline only and not a guarantee of compensation or salary. Additional factors considered in extending an offer include (but are not limited to) responsibilities of the job, education, experience, knowledge, skills, and abilities, as well as internal equity, alignment with market data, applicable bargaining agreement (if any), or other law.