Senior Full Stack AI Engineer (US Citizen)

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, leading the design, development, and delivery of large-scale mission-critical AI systems. - Serve as a senior technical lead, defining AI and application architecture for the platform in partnership with and under the direction of the Director. - Establish enterprise modernization roadmaps aligned to mission outcomes, compliance, and scalability. - Lead architecture for distributed, cloud-native, and hybrid AI systems. - Define and enforce reference architectures, standards, and reusable frameworks. - Drive cross-program technical decision-making to ensure interoperability, security, and long-term sustainability. - Lead design, development, and deployment of advanced AI solutions, including large language models (LLMs) and foundation models, Retrieval-Augmented Generation (RAG) systems, agentic workflows, and orchestration frameworks. - Architect and implement scalable AI applications and services using Next.js, cloud-native APIs, and managed AI services across AWS, Azure, and GCP. - Build full-stack AI applications end to end, from user-facing interfaces to back-end services, APIs, and data layers. - 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. - Oversee the full AI solution lifecycle: data pipelines, evaluation, deployment, and monitoring. - Drive LLM performance and cost optimization (e.g., caching, prompt and context optimization, model selection). - Establish robust MLOps practices leveraging GitHub-based automation, CI/CD 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 refactoring and re-platforming efforts). - Define repeatable modernization frameworks and accelerators. - Oversee DevSecOps pipelines and CI/CD automation (e.g., GitHub Actions), 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 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). 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 AI and LLM systems built on a modern cloud stack (Next.js, Terraform, GitHub, AWS, Azure, and GCP), 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 full-stack development experience, including designing production-grade AI systems, data pipelines, and microservices-based architectures. - 6+ years of significantly demonstrated leadership responsibility experience. - Expert-level proficiency across the platform stack (Next.js, Terraform, GitHub, AWS, Azure, and GCP), including building large-scale AI applications, APIs, and data pipelines. - Full-stack engineering skills, including modern front-end frameworks (e.g., Next.js/React), back-end services, RESTful APIs, microservices, and cloud-native deployment (e.g., containers, Kubernetes). - Deep expertise in LLMs and generative AI, including transformer-based model architectures and their practical application. - 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. - Expertise designing AI systems in cloud-native, distributed environments across AWS, Azure, and GCP. - Proficiency with infrastructure as code, including Terraform, for provisioning and managing cloud environments. - 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. - 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. - Demonstrated ability to translate AI research into production systems. - 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. - 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. $159k -$201k.