Senior Data AI Engineer
IntelliTech
- Alexandria, Virginia, United States
- Alexandria, Virginia, United States
About
Remote
Clearance:
Active DoD Secret clearance required
Employment Type:
Full-Time (W-2)
Citizenship:
U.S. Citizenship required
IntelliTech is seeking a
Senior Data / AI Engineer
to support a Department of War program focused on operationalizing a Government‑owned digital twin application for ammunition industrial base readiness. The platform is a supply chain simulation solution built on
Python, FastAPI, and React
that enables analysts to model production timelines, identify bottlenecks, assess supply chain risk, and evaluate surge and modernization scenarios.
This role will own the data lifecycle end‑to‑end—from raw file ingestion through validation, normalization, versioning, and delivery of run‑ready artifacts to the simulation engine. The engineer will also help design and implement the
AI‑enabled decision‑support layer , supporting natural‑language analysis of scenario outputs, automated comparison and briefing generation, and guided scenario creation.
This is a hands‑on role on a lean, senior team. The ideal candidate is comfortable writing production code daily, designing scalable data pipelines, and working directly with Government analysts and data stakeholders to deliver mission‑focused solutions.
Key Responsibilities Data Ingestion and Automation
Design and implement governed ingestion pipelines for complex defense supply chain datasets, including Bills of Materials (BOM), demand and order backlogs, facility and production line capacity, supplier risk, and acquisition planning data
Build validation services that enforce schema conformance, referential integrity across linked datasets, circular reference detection, and business‑rule validation with actionable row‑ and column‑level feedback
Implement raw data preservation in object storage such as Amazon S3, including metadata capture for source type, upload timestamp, uploader identity, file checksum, and dataset version
Develop canonical data transformation workflows that convert validated source inputs into normalized, run‑ready artifacts aligned to the simulation engine’s entity model
Implement dataset versioning and lineage tracking so each scenario run is tied to explicit input versions and assumptions
Automated Data Refresh
Work with Government stakeholders and source‑system owners to identify, prioritize, and implement automated or semi‑automated data refresh paths
Participate in Technical Exchange Meetings (TEMs) to help define data contracts, including source format, semantics, refresh cadence, and validation requirements
Implement approved connection patterns such as scheduled file landing, secure file exchange (SFTP), API‑based retrieval, and cloud‑to‑cloud transfer mechanisms
Maintain hardened controlled upload workflows in parallel so mission operations are not dependent solely on external integrations or approvals
AI‑Enabled Decision Support
Build the AI integration layer within the FastAPI backend to broker access to Government‑approved hosted LLM endpoints
Implement scoped retrieval logic that constrains AI context to approved run artifacts, simulation outputs, and post‑processed analytics
Develop natural‑language Q&A capabilities that allow analysts to query scenario results such as bottlenecks, supplier risks, and differences between runs
Build guided scenario generation workflows that translate analyst intent into structured JSON scenario configurations for user review and approval before execution
Implement AI‑assisted comparison summaries and brief‑ready output generation
Enable function calling and tool‑use patterns so the model can dynamically query backend APIs for scenario comparison, bottleneck analysis, production planning, and supply chain risk
Ensure all AI interactions are audit‑logged, role‑scoped, and grounded in explicit scenario artifacts
Deterministic Analytics and Reporting
Extend existing comparison capabilities to generate structured side‑by‑side scenario outputs with standardized metrics and deltas
Build reusable templates for brief‑ready outputs that reduce analyst time‑to‑brief
Generate reproducible comparison artifacts and store them as part of the scenario run record
Data Quality and Performance
Implement data quality monitoring and dashboards for ingestion success rates, validation outcomes, and overall pipeline health
Optimize data preparation and post‑processing workflows to reduce end‑to‑end scenario runtime
Design and implement version‑bounded caching strategies for validated inputs, normalized data products, and reusable post‑processing summaries
Required Qualifications
Bachelor’s degree in Computer Science, Data Science, Engineering, Information Systems, or a related technical discipline and 8+ years of relevant experience; or Master’s degree in a related field and 6+ years of relevant experience
Active DoD Secret clearance
7+ years of professional experience in data engineering or data / AI engineering roles
Strong hands‑on Python development experience, including Pandas, NumPy, ETL/ELT design, data pipeline development, and asynchronous programming patterns
Experience building data validation and quality frameworks, including schema enforcement, referential integrity, data contracts, and validation feedback mechanisms
Experience integrating LLM APIs such as OpenAI, Anthropic, or equivalent platforms, including function calling, tool use, scoped retrieval, and prompt engineering for structured outputs
Experience with MongoDB or other document‑oriented databases, including data modeling and aggregation pipelines for analytics workloads
Experience with Amazon S3 or other cloud object storage services, including raw, normalized, and curated data layering approaches
Experience supporting DoD or federal Government programs
Strong communication skills and the ability to work directly with technical and non‑technical stakeholders in mission environments
Preferred Qualifications
Experience with defense supply chain, logistics, manufacturing, or industrial base data
Familiarity with Databricks, data mesh, or medallion architecture patterns such as bronze/silver/gold
Familiarity with SimPy or discrete‑event simulation data inputs and outputs
Experience with Advana, WDP (War Data Platform), or other DoD enterprise data platforms
Experience establishing data‑sharing agreements and supporting Technical Exchange Meetings with Government source‑system owners
Knowledge of munitions‑related data structures such as NIIN, CAGE, Bill of Material hierarchies, and production line capacity models
Experience with Redis or other caching layers supporting analytics applications
Experience with FastAPI or Flask backend development
Prior experience supporting Army Cloud Environments
Tech Stack
Data Engineering: Python 3.11+, Pandas, NumPy
Backend: FastAPI, Motor (async MongoDB)
AI / LLM: OpenAI API or Government‑approved hosted endpoint, function calling, scoped retrieval, prompt engineering
Database: MongoDB / Amazon DocumentDB
Storage: Amazon S3
Cache: Redis / Amazon ElastiCache
Data Formats: Excel (.xlsx), JSON, CSV, SFTP, REST / SOAP APIs
Observability: Pipeline instrumentation, logging, and data quality metrics
Interview Process Video interview required
and may include a technical assessment.
Candidates should be prepared to discuss:
their hands‑on experience building data pipelines, validation frameworks, and AI‑enabled backend services
examples of systems or applications they have built from scratch
how they have handled data quality, lineage, and reproducibility in production environments
their experience integrating LLMs, retrieval workflows, and backend APIs into operational use cases
their work with large‑scale or mission‑critical federal datasets and analytics platforms
their availability to support periodic on‑site work in the Washington, DC Metro Area or other Government locations as needed
Compensation And Benefits IntelliTech is committed to fair and equitable compensation practices. Actual compensation packages are based on several factors unique to each candidate, including but not limited to job‑related skills, depth of experience, relevant certifications and training, and specific work location. Based on these factors, IntelliTech utilizes the full width of the salary range.
IntelliTech provides a comprehensive benefits package designed to support employees’ well‑being and professional growth, including health insurance, dental insurance, and vision insurance, a 401(k), paid time off, professional development opportunities, and flexible work arrangements to support work‑life balance.
About IntelliTech IntelliTech is a dynamic and forward‑thinking small business specializing in
Full Stack Engineering, Data Analytics, Cloud Solutions, and DevSecOps
services. Our mission is to empower government and commercial clients to solve complex technical challenges through practical, innovative, and mission‑focused engineering solutions.
Equal Opportunity Employer At IntelliTech, we are committed to building a diverse and inclusive workplace. We believe that a variety of perspectives and backgrounds leads to stronger teams and better solutions. IntelliTech is an Equal Opportunity Employer and does not discriminate on the basis of race, religion, gender, age, disability, or veteran status. We encourage all qualified candidates to apply.
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Languages
- English
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