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Job Type
Full-time
Description
NOTE: This opportunity is full-time employment position only (no 1099 or C2C engagements, or third parties or staffing agencies, please). The candidate MUST be a U.S. Citizen). This is a remote opportunity; candidate must be based in the U.S.; preference given to candidates in the Washington DC, Maryland, Virginia (DMV) area; ET zone work schedule.
Daily Responsibilities
- Serve as a senior, hands-on full-stack AI engineer and technical authority, leading the technical strategy, design, and delivery of large-scale mission-critical AI systems supporting federal programs.
- Serve as the primary technical authority, defining AI and application architecture across multiple programs.
- Lead architecture for distributed, cloud-native, and hybrid AI systems.
- Define and enforce reference architectures, standards, and reusable frameworks.
- Establish enterprise modernization roadmaps aligned to mission outcomes, compliance, and scalability.
- Drive cross-program technical decision-making to ensure interoperability, security, and long-term sustainability.
- Advise senior federal stakeholders (SES-level and above) on AI adoption, modernization, and risk management.
- Build full-stack AI applications end to end, from user-facing interfaces to back-end services, APIs, and data layers.
- Lead design, development, and deployment of advanced AI solutions using Python as the primary development language, including large language models (LLMs) and foundation models, Retrieval-Augmented Generation (RAG) systems, agentic workflows, and orchestration frameworks.
- Architect and implement scalable ML systems and services built on Python-based frameworks and APIs.
- Integrate AI and LLM capabilities into existing enterprise applications and legacy platforms (e.g., content management, case management, and records systems) via APIs, middleware, and event-driven patterns.
- Define and implement distributed training strategies (GPU/TPU clusters, parallelization, optimization).
- Oversee full ML lifecycle in partnership with the senior data scientist: data pipelines, feature engineering, training, evaluation, deployment, and monitoring.
- Drive model optimization techniques (quantization, distillation, caching) to improve performance and cost.
- Establish robust MLOps practices leveraging Python-driven automation, pipelines, and tooling.
- Stand up the enterprise CI/CD-to-AI/MLOps pipeline, beginning with time-boxed proofs of concept and MVP implementations that mature into production systems.
- Serve as SME in federal AI policy (e.g., NIST AI RMF, OMB M-25-21 and M-25-22, Executive Order 14179).
- Define and operationalize Responsible AI frameworks, including model validation and evaluation, bias mitigation and fairness, and explainability, auditability, and safety.
- Ensure compliance with FISMA, FedRAMP, NIST 800-53, privacy, and Section 508 requirements.
- Lead large-scale modernization initiatives (e.g., legacy-to-cloud, microservices transformation, including Python-based refactoring and re-platforming efforts).
- Define repeatable modernization frameworks and accelerators.
- Oversee DevSecOps pipelines, CI/CD automation, zero-trust architectures, and secure software supply chain practices.
- Ensure delivery of resilient, high-availability systems in regulated federal environments.
- Lead multiple concurrent engineering efforts across integrated teams.
- Provide technical leadership to architects, engineers, and DevSecOps specialists, including establishing Python coding standards and engineering best practices.
- Mentor senior engineers and technical leaders; elevate engineering excellence and code quality.
- Support technical strategy in proposals, captures, and client engagements.
- Contribute to thought leadership (whitepapers, architecture patterns, platform strategy).
- Executive communication skills with experience influencing senior leaders.
Requirements
- Must of a U.S. Citizen.as mandated by our government client.
- Must be able to complete/pass/hold at a minimum a Public Trust Investigation / background check. An active clearance (e.g. Public Trust, Secret, or higher) is preferred.
- Must be based / reside in the U.S.
- 12+ years of software engineering experience combining senior technical leadership, hands-on expertise in Python-based AI/ML systems (including large language models), and ownership of enterprise architecture, governance, and innovation.
- 8+ years of applied AI/ML experience, including building and deploying production systems (LLMs, generative AI, and large-scale or distributed model systems).
- Expert-level Python development experience, including designing production-grade ML systems, data pipelines, and microservices-based architectures.
- Hands-on experience with ML frameworks (PyTorch, TensorFlow, JAX) and distributed training (DeepSpeed, FSDP, Horovod).
- Full-stack engineering skills, including front-end frameworks, back-end services, RESTful APIs, microservices, and cloud-native deployment (e.g., containers, Kubernetes).
- Deep experience with cloud platforms (Azure, AWS, GCP), including FedRAMP environments and designing AI systems in cloud-native, distributed environments.
- Deep expertise in machine learning and deep learning, particularly transformer-based models and LLMs.
- Hands-on experience with LLM application stacks, including orchestration frameworks (e.g., LangChain, LlamaIndex, Semantic Kernel), embeddings, vector databases, and prompt engineering.
- Experience with AI platforms and architectures (e.g., AWS Bedrock, Azure OpenAI Service, Google Vertex AI, RAG, agents).
- Proven success delivering enterprise-scale systems and modernization programs.
- Strong background in microservices, APIs, distributed systems, and DevSecOps practices.
- Experience managing GPU-based infrastructure or high-performance ML environments.
- Demonstrated ability to translate AI research into production system.
- Proven ability to integrate AI capabilities into existing and legacy enterprise systems (e.g., legacy CMS or COTS platforms) using APIs, middleware, connectors, and event-driven architectures.
- Strong understanding of large-scale data systems and ML evaluation methodologies.
- Experience working with sensitive data, including PII safeguards such as anonymization, masking, and data loss prevention.
- Experience with enterprise integration technologies, including REST/SOAP services, message queues, ETL pipelines, and SQL/NoSQL databases.
- Proficiency with managed generative AI services (e.g., AWS Bedrock, Azure OpenAI Service, Google Vertex AI) and integrating frontier models such as GPT, Claude, and Gemini.
- Demonstrated ability to own solutions end to end - from discovery and prototyping through production deployment, integration, and ongoing support.
- Ability to balance strategic vision with deep hands-on technical execution.
- Excellent analytical skills, attention to detail, and strong problem-solving abilities.
- Excellent communication and collaboration skills to communicate complex analytical insights to executive and non-technical stakeholders. Ability to translate ambiguous business questions into analytical solutions.
- Experience with federal civilian agencies preferred.
- BS or MS degree (preferred) in engineering, data science, computer science, statistics or related field.
Compensation decisions depend on a wide range of factors, including but not limited to skill sets, experience and training, security clearances, licensure and certifications, and other business and organizational needs. $155k to $175k.
Salary Description
$155k - $175k