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Software Development Engineer II, Front End
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The Dealer team at CarGurus is looking for a Software Engineer II to advance our Inventory products. This role involves transforming CarGurus market data into impactful experiences for…
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Senior AI Software Developer & Engineer (US Citizen)

Remote, United States

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Job details

Experience: 8+ years of applied AI/ML experience, including building and deploying production systems (LLMs, generative AI, and large-scale or distributed model systems).
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 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

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More about this job

Skills

Regulatory compliance
Decision making
Risk management
Python
ML
CI/CD
Microservices
Supply chain management
Leadership
Communication skills
PyTorch
TensorFlow
API development
Kubernetes
Microsoft Azure
AWS
Google Cloud
Machine learning
Deep learning
LangChain
Research
CMS
Loss prevention
Data engineering
SQL
NoSQL
Analytical skills
Attention to detail
Problem solving
Collaboration
Computer science
Statistics

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