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VoIP Engineer
TekSynap
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Install, configure, and maintain telephone system hardware, cabling, software, and associated infrastructure.
Added 1 min ago
Software Test Engineer I
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Medtronic is seeking a motivated and detail-oriented Software Test Engineer to join our Affera Mapping and Ablation System team. This role is essential in supporting the testing and quality…
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Research & Development Engineer I - Onsite
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Software Engineer II
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Senior Scientist II / Principal Research Scientist I - Analytical R&D
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Added 6 mins ago
Associate Software Engineer
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Asset & Wealth Management - Software Engineering - Vice President - New York
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Added 7 mins ago
Senior Full Stack AI Engineer (US Citizen)
Remote, United States
Serve as a senior, hands-on full-stack AI engineer, leading the design, development, and delivery of large-scale mission-critical AI systems.
Added 7 mins ago
Senior AI Software Developer & Engineer (US Citizen)
Remote, United States
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Staff DevOps Engineer
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AWS Databricks Engineer
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Test & Characterization Senior Engineer
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Test Engineer
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Facilities Building Engineer
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Senior Full Stack AI Engineer (US Citizen)

Remote, United States

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Job details

Experience: 6+ years of significantly demonstrated leadership responsibility experience.
Posting language: English
Posted: Nov 5, 2026

Original job description

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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, 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. $155k-$175k.

Salary Description

$155k - $175k

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More about this job

Skills

Regulatory compliance
Decision making
GitHub
CI/CD
Microservices
Supply chain management
Leadership
Next.js
IBM Terraform
AWS
Microsoft Azure
Google Cloud
ML
Full-stack development
React
API development
Kubernetes
CMS
Loss prevention
Data engineering
SQL
NoSQL
Infrastructure as code
LangChain
Research
Analytical skills
Attention to detail
Problem solving
Communication skills
Collaboration
Computer science
Statistics

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