AI Engineer

• Define and evolve the standards, architecture patterns, and tooling choices that govern how engineers use AI across the codebase — including where to invest in codebase readiness (instrumentation, specification, evaluation) versus targeted rewrites.
• Own execution of the multi-milestone plan (readiness → validated → governed) applied to corgi and secondary repos, in partnership with squad leads — without being their manager.
• Decide what AI tooling, agent infrastructure, and evaluation pipelines the org needs, and build or oversee the build of the ones that don't exist yet.
• Set the policy and technical controls for where human-in-the-loop review is required versus where autonomous agent action (PR review, deployment) is safe — resolving the tension between adoption speed and security/quality risk.
• Write the guidance engineers actually use — AGENTS.md/CLAUDE.md conventions, review norms for AI-generated code, eval standards — and make sure it's adopted, not just published.

AI Architect

• Design, build, test, and deploy AI legal workflows and skills across firm-approved AI tools, supporting use cases such as contract review, due diligence, drafting, research, and summarization
• Partner directly with attorneys and knowledge management professionals to identify high-value use cases, map workflows, and translate practice needs into structured AI solutions
• Develop and maintain retrieval pipelines, document templates, and reusable workflow components with clear, attorney-ready documentation
• Build and execute evaluation frameworks to measure workflow quality, accuracy, and reliability; monitor performance and iterate based on feedback and outcomes
• Serve as a go-to resource for enablement and change management for developed workflows, partnering with the AI Adoption team to drive usage, deliver training sessions and workshops, 1:1 coaching, and develop practical playbooks and materials that support sustained adoption
• Develop and scale best-practice standards, use-case libraries, and playbooks that empower attorneys to responsibly and independently leverage AI tools while maintaining quality and risk controls
• Embed human-review checkpoints and audit mechanisms aligned with Firm governance, confidentiality, and professional responsibility requirements
• Collaborate closely with cross-functional stakeholders including the AI Product team, AI Adoption team, Knowledge & Innovation team, IT, Innovation Practice Group, and Professional Development to ensure aligned delivery and support
• Track adoption, usage, and impact metrics for deployed workflows and recommend refinements or expansion opportunities
• Manage ongoing maintenance of production workflows, including updates to prompts, retrieval sources, and guardrails in response to model and platform changes, user feedback, or evolving practice needs
• Represent the AI Products & Adoption team in internal meetings, committees, and practice group sessions

AI Legal Engineer

• Evaluate emerging advancements across hyperscalers, leading AI labs, academia, and venture-backed companies.
• Conduct hands-on technical assessments, benchmarks, and proof-of-concepts to validate applicability and performance.
• Own the technical delivery of end-to-end AI systems spanning infrastructure, frontier LLMs, agentic and multi-agent applications and physical AI pipelines.
• Work with business units, to support hands-on integration of emerging AI technologies in complex customer environments such as cloud, hybrid, edge, or disconnected.
• Provide day-to-day technical leadership to engineers and researchers.
• Conduct design reviews, guide architectural decisions, and raise the overall technical bar of the Applied Research team.
• Convert prototype learnings into reusable frameworks, reference architectures, accelerators, and technical artifacts that scale across programs and business units.

Lead AI Research Engineer

• Build full-stack AI applications using React, JavaScript/TypeScript, and Python
• Develop both rapid prototypes and production-ready applications
• Integrate AI capabilities using OpenAI APIs and related AI services
• Design and implement frontend interfaces, backend APIs, database interactions, and application workflows
• Deploy and manage applications using tools such as Vercel and AWS
• Work with Supabase for backend services such as authentication, database management, storage, and APIs
• Use AI-assisted development tools such as Cursor, v0, Codex, and Claude Code to accelerate development
• Debug, test, document, and improve applications for performance, reliability, and usability
• Collaborate with product, strategy, design, and technical teammates to move ideas from concept to shipped product

AI Full Stack Developer

• Design, build, and deploy AI-powered applications including chatbots, knowledge assistants, and workflow automation agents.
• Implement end-to-end solutions covering data ingestion, transformation, prompt orchestration, model interaction, and cloud deployment.
• Integrate AI systems with internal APIs, enterprise platforms, and data pipelines.
• Design agent workflows with tool/function calling, branching logic, retries, and fallback handling.
• Implement human-in-the-loop and approval-based workflows for regulated financial use cases.
• Build multi-agent systems for validation, refinement, and complex task decomposition.
• Design and implement RAG pipelines covering chunking, embeddings, retrieval, and grounding.
• Work with structured and unstructured data using SQL, S3, and data pipeline tools.
• Leverage AWS services (S3, Glue, Redshift, Lambda, ECS, Step Functions, SQS/SNS) for storage, transformation, and orchestration.
• Monitor and improve AI systems for accuracy, latency, cost, and reliability.
• Implement structured output validation, schema enforcement, and guardrails.
• Evaluate model performance and iteratively improve grounding and output consistency.

AI Engineer – Financial Services

• The Lead, Network Engineer designs, analyzes, plans and modifies network components supporting customer communication implementation activities.
• Work on problems of diverse scope and complexity ranging from moderate to substantial.
• Be the technical SME for our enterprise WAN, remote LAN/WLAN, and data center SD-WAN network services.
• Responsible for engineering, implementing, and operating a multi-vendor SD-WAN environment, scalable campus/branch switching and wireless, and resilient remote branch solutions.
• Serve as the escalation point for complex routing, overlay, and authentication issues, including RADIUS/TACACS+.
• Provide technical leadership to a team of network engineers.
• Work closely with the Lead Network Architect and Principal Network Architect through structured technical solution peer reviews.

Lead Network Engineer – AI

• Build and maintain shared infrastructure: secure web app hosting infrastructure, MCP/API standards, secret/key management, model selection, guardrails, and a centralized pattern library that all builders across the organization can pull from.
• Own the review, tracking, and advisory process for all corporate AI and automation builds — MCP servers and tool configurations, Claude Skills, API integrations, and agentic workflows — before deployment.
• Partner directly with AI specialists across departments to identify, scope, and build AI and automation solutions that meet high standards for cost, security, and scalability security before they ship.
• Define and maintain the secure intake path for AI and automation requests across the organization, replacing ad-hoc builds and shadow IT with a governed, cost-effective, and scalable process.
• Contribute to the organization’s AI cost efficiency by monitoring, alerting, and setting thresholds on AI spend across usage-based tools and platforms.

AI & Automations Lead

• Work closely with business stakeholders to rapidly develop, demonstrate, and refine AI solutions.
• Take a hands-on engineering approach focused on building rather than advising.
• Work directly with the client to translate business problems into AI-enabled prototypes and production-ready solutions.

• Monitor and analyze emerging AI policy, regulatory, and legislative developments across the United States and key international markets
• Develop policy positions, briefing materials, white papers, and executive-level recommendations on issues related to artificial intelligence, national security, technology competitiveness, workforce transformation, and digital infrastructure
• Build and maintain relationships with policymakers, government agencies, think tanks, industry associations, and academic institutions
• Support executive engagement with Congress, the Executive Branch, regulators, and international government stakeholders
• Represent Cognition in policy discussions, industry working groups, conferences, and strategic forums
• Partner with technical teams to translate complex AI capabilities into clear, actionable policy narratives
• Identify opportunities for public-private partnerships that accelerate AI adoption across government and critical industries
• Help anticipate policy risks and opportunities that could impact company strategy, product development, or market expansion

AI Policy Lead

• Contribute to the design of AI-enabled solution architectures within the Avanade Catalyst Solutions portfolio
• Apply and help develop reusable patterns, reference architectures, and accelerators
• Adapt pre-built AI solutions to support client needs and evolving market requirements
• Ensure solutions meet standards for scalability, security, and production readiness
• Collaborate with engineering and delivery teams to identify opportunities for AI-driven tools and methods
• Support the development of repeatable, outcome-based services
• Help drive consistency in how AI solutions are implemented across engagements
• Support architecture and deployment of ready-made AI offerings
• Partner with security, risk, and compliance teams to align with enterprise requirements
• Provide guidance to delivery teams

Manager, Global AI Enterprise Architect

• Design and build GenAI use cases based on client's needs and specific data
• Understanding of Agentic AI frameworks and design and when they are appropriate for use
• Develop and deploy generative AI models using platforms such as Amazon Kendra, Google Vertex AI, and AWS Bedrock.
• Implement large language models (LLMs) and transformer models to create advanced AI applications.
• Integrate solutions with cloud-based AI services such as Google Cloud Document AI, Google Cloud Gemini, Google Cloud Gemma, and Google Cloud Generative AI Language Models.
• Show your curiosity, motivation, and passion for experimentation and continuous learning to drive AI innovation that is applicable across enterprise client domains.

GenAI Engineer

• Own two of the most strategically important product surfaces in our platform: AI-Native Intelligence Dashboard and Payments & Monetization
• Lead the team of senior engineers, set technical direction, and ship AI-powered capabilities that customers can see, feel, and measure
• Multiply your impact by leading, mentoring, and developing Engineering Managers and senior engineers
• Own the outcome for execution across multiple AI engineering teams
• Drive an AI First approach to engineering
• Practice Change Agility by guiding teams through evolving AI technologies
• Apply First Principles Problem Solving to complex AI and product challenges
• Leverage Data-Driven Decision Making to assess system performance
• Collaborate cross-functionally with Product, Design, Data, Security Research, and other Engineering leaders

Senior Manager, AI Engineering

• Design and build agentic AI workflows that can plan and execute complex, multi-step tasks
• Build and maintain RAG solutions end-to-end
• Implement retrieval evaluation and continuous improvement loops
• Integrate tools and data sources using MCP or equivalent standard interfaces
• Deploy and operate AI services on AWS with strong operational practices
• Engineer robust runtime behavior including error handling, retries, fallbacks
• Enable workflow automation and integration by identifying high-value repetitive processes
• Create and maintain technical documentation for architectures, interfaces, evaluation approaches
• Collaborate cross-functionally with product/business stakeholders to translate workflows into well-scoped deliverables

• Manage mission-critical collaboration and content management systems
• Integrate AI capabilities such as intelligent search, workflow automation, and context-aware assistance
• Collaborate closely with stakeholders, system owners, security teams, and end users
• Stay current with Department AI initiatives and systems
• Design, develop, test, implement, and deploy SharePoint infrastructure and solution components
• Drive innovation within the M365 and SharePoint environments
• Conduct stakeholder consultations and workshops

M365 Automation and AI Developer

• Design and build agentic AI workflows that can plan and execute complex, multi-step tasks
• Build and maintain RAG solutions end-to-end
• Implement retrieval evaluation and continuous improvement loops
• Integrate tools and data sources using MCP or equivalent standard interfaces
• Deploy and operate AI services on AWS
• Engineer robust runtime behavior
• Enable workflow automation and integration
• Create and maintain technical documentation
• Collaborate cross-functionally with product/business stakeholders
• Other duties as assigned

• Design and build agentic AI workflows that can plan and execute complex, multi-step tasks
• Build and maintain RAG solutions end-to-end
• Implement retrieval evaluation and continuous improvement loops
• Integrate tools and data sources using MCP or equivalent standard interfaces
• Deploy and operate AI services on AWS with strong operational practices
• Engineer robust runtime behavior including error handling, retries, fallbacks, circuit breakers, and guardrails
• Enable workflow automation and integration by identifying high-value repetitive processes
• Create and maintain technical documentation for architectures, interfaces, evaluation approaches, and operational runbooks
• Collaborate cross-functionally with product/business stakeholders to validate outcomes against measurable success criteria
• Other duties as assigned.

• Turn quantitative prototypes into reusable tools, services, packaged desktop applications, interfaces, and workflow products that can move from internal demo to client pilot to scaled offer.
• Ship across both cloud-hosted services and locally distributed desktop applications, including Electron-based apps when the workflow or client environment calls for it.
• Build enterprise hardening into the productization layer, including authentication, role-based access control (RBAC), observability, security, release quality, cost controls, and deployment discipline.
• Build evaluation, regression, and release discipline into the productization layer so model logic and agent behavior remain measurable as systems change.
• Work closely with the quant lead so model logic, evaluation intent, and governance requirements survive the move into production.
• Make pragmatic architecture choices across large language models (LLMs), deterministic rules, and hybrid systems based on value, latency, cost, and reliability.
• Help shape repeatable build patterns so strong prototypes become faster, more reliable, and more reusable over time.

AI Engineer jobs in District of Columbia

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AI Architect

AI Legal Engineer

Lead AI Research Engineer

AI Full Stack Developer

AI Engineer – Financial Services

Lead Network Engineer – AI

AI & Automations Lead

AI Engineer

AI Policy Lead

Manager, Global AI Enterprise Architect

GenAI Engineer

Senior Manager, AI Engineering

AI Engineer

M365 Automation and AI Developer

AI Engineer

AI Engineer

QuantAI Engineer – Trading, Investment & Optimization