{"id":2506892,"url":"https://alion.io/job/matrix-ifs-lead-ai-enablement-engineer","title":"Lead AI Enablement Engineer","company":{"id":4430251,"name":"page - Matrix","domain":"matrix-ifs.com","url":"https://alion.io/company/matrix-ifs","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Comeet","truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":"lead","employment_type":"full_time","work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":150000,"max_usd":306000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":691},"experience_years_min":7,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"CI/CD","optional":false},{"name":"Claude Code","optional":false},{"name":"Function Calling","optional":false},{"name":"Git","optional":false},{"name":"LLM","optional":false},{"name":"Model Context Protocol","optional":false},{"name":"RAG","optional":false},{"name":"Tool Use","optional":false},{"name":".NET","optional":true},{"name":"Anthropic","optional":true},{"name":"AWS","optional":true},{"name":"AWS Bedrock","optional":true},{"name":"C#","optional":true},{"name":"Embeddings","optional":true},{"name":"Go","optional":true},{"name":"Human-in-the-Loop","optional":true},{"name":"OpenAI","optional":true},{"name":"Prompt Engineering","optional":true},{"name":"Python","optional":true},{"name":"Semantic Search","optional":true},{"name":"Semantic Search","optional":true},{"name":"Slack","optional":true},{"name":"TypeScript","optional":true}],"status":"live","first_seen_at":"2026-10-12T17:15:20Z","employer_posted_date":"2026-10-12","last_verified_at":"2026-10-15T18:09:54Z","board_verified":true,"closed_at":null,"days_open":3,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":3},"description":"Description\nLead AI Enablement Engineer\nPosition Summary\nWe are seeking a Lead AI Enablement Engineer to drive the adoption and evolution of AI-assisted engineering practices across product engineering teams. This is a hands-on individual contributor role within the Platform team, partnering closely with the Platform Lead to improve developer productivity, software quality, and delivery efficiency through practical AI enablement.\nThe ideal candidate combines strong software engineering fundamentals with deep, hands-on experience using AI coding tools, LLMs, and agent-based workflows. You will establish reusable AI-first development practices, integrate AI workflows into the software delivery lifecycle, and build the foundations for validated, sandboxed, and orchestrated agent execution.\nYou will also help shape reusable AI patterns for customer-facing product capabilities and internal employee tools, balancing innovation with security, governance, reliability, and measurable engineering outcomes.\nKey Responsibilities\nAI-First Engineering Enablement\nEstablish and scale AI-assisted workflows across the software development lifecycle, including planning, coding, debugging, testing, design, documentation, code reviews, and production investigation.\nDevelop reusable agent instructions, prompts, skills, repository context, integrations, and reference implementations that engineering teams can adopt directly.\nEvaluate emerging AI coding tools, LLM platforms, and agent frameworks to identify opportunities to improve engineering productivity and software delivery.\nHelp teams transition from ad hoc, locally executed AI usage toward structured, repeatable, and validated AI-assisted workflows.\nPlatform Integration and Agent Orchestration\nPartner with the Platform Lead to integrate AI workflows with CI/CD pipelines, automated testing, development sandboxes, observability, release readiness, and developer productivity tooling.\nDevelop patterns for orchestrating multiple AI agents and parallel workflows, with appropriate human oversight and validation.\nBuild integrations with developer tools, repositories, APIs, and internal systems to provide AI agents with relevant context and controlled access to resources.\nContribute to the evolution of sandbox-based agent execution environments that support safe, reliable, and scalable engineering workflows.\nQuality, Validation, and Governance\nDesign validation loops that ensure AI-generated code and other outputs meet engineering standards through automated tests, code reviews, security checks, and release controls.\nEstablish evaluation methods and quality signals to assess the accuracy, reliability, usefulness, and consistency of AI-assisted workflows.\nApply sound engineering judgment to security, privacy, access control, auditability, operational risk, and AI usage costs.\nCollaborate with engineering and platform stakeholders to ensure AI adoption improves outcomes without compromising production reliability or software quality.\nProduct and Internal AI Capabilities\nDefine reusable architectural patterns for AI-powered product features and internal employee capabilities.\nSupport the development of AI assistants, retrieval-augmented generation (RAG) solutions, knowledge agents, workflow automation, and tool-enabled applications.\nEstablish shared patterns for agent integrations, context management, permissions, governance, and reusable AI components.\nPartner with product and engineering teams to identify opportunities to embed AI into existing workflows and customer-facing capabilities.\nDeveloper Enablement and Continuous Improvement\nDrive adoption through hands-on demonstrations, pairing sessions, workshops, documentation, examples, and engineering office hours.\nCoach engineers on effective AI-assisted development practices, agent orchestration, and output validation.\nMeasure the impact of AI enablement using adoption, productivity, delivery speed, software quality, developer experience, and cost metrics.\nContinuously refine workflows based on engineering feedback, evaluation results, and measurable business outcomes.\nRequired Qualifications\n 7+ years of software engineering experience preferred, with consideration for strong senior-level candidates who demonstrate exceptional hands-on AI engineering and enablement capabilities.\nSignificant practical experience using AI tools for real-world software engineering, including AI coding assistants, agentic development workflows, and LLM-powered developer tools.\nHands-on experience with tools such as Claude Code, Warp, or comparable AI coding and engineering platforms.\nStrong software engineering fundamentals, including application development, testing, code review, debugging, and production support.\nExperience building reusable developer workflows, integrations, automation, agent instructions, prompt patterns, skills, or reference implementations.\nWorking knowledge of LLM APIs, tool calling, agent orchestration, and Model Context Protocol (MCP) or comparable integration approaches.\nUnderstanding of modern software delivery practices, including Git, CI/CD, automated testing, release management, and production readiness.\nAbility to evaluate AI-generated outputs critically and implement appropriate testing, review, security, and validation mechanisms.\nStrong communication and collaboration skills, with the ability to influence engineering practices and drive adoption across teams.\nA practical, AI-first mindset focused on measurable improvements in engineering productivity, delivery quality, and developer experience.\nTechnology and Tools\nExperience with several of the following technologies and capabilities is highly desirable:\nAI Engineering and Agent Development\nClaude Code, Warp, AI coding agents, and LLM-powered developer workflows.\nAgent SDKs and APIs from Anthropic, OpenAI, AWS Bedrock, or comparable platforms.\nMCP servers, tool calling, agent orchestration, reusable instructions, prompt engineering, and agent skills.\nSoftware Engineering and Developer Productivity\nProficiency in at least one modern programming language, such as Python, TypeScript, Go, C#, or .NET.\nGit-based development workflows, CI/CD pipelines, test automation, code review, and release management.\nDeveloper productivity platforms, internal tooling, and engineering workflow automation.\nAI Applications and Knowledge Systems\nRetrieval-augmented generation (RAG), semantic search, embeddings, vector databases, and knowledge agents.\nAI assistants, workflow automation, and integrations with internal systems and collaboration tools.\nCloud, Infrastructure, and Observability\nAWS or comparable cloud platforms, containers, and sandboxed execution environments.\nLogging, tracing, monitoring, observability, AI evaluation, and cost tracking.\nSecurity controls, identity and access management, privacy, auditability, and governance for AI systems.\nPreferred Qualifications\nExperience implementing sandboxed or cloud-based agent execution and parallel AI workflows.\nFamiliarity with human-in-the-loop orchestration and validation of autonomous or semi-autonomous agents.\nExperience building AI-powered SaaS features, internal knowledge-base agents, Slack integrations, or employee AI assistants.\nExperience modernizing legacy applications or engineering workflows in a mature production SaaS environment.\nDomain experience in fintech, payments, accounting, eCommerce, property management, or other transactional businesses.\nExperience working in security-conscious, compliance-driven, or regulated environments.\nSuccess Metrics\nSuccess in this role will be measured by the ability to:\nEstablish repeatable, AI-first engineering workflows that product teams can adopt across common development activities.\nDemonstrate measurable improvements in engineering productivity, delivery speed, software quality, review confidence, and developer experience.\nImprove the relevance and reliability of AI-generated outputs through better repository context, business rules, engineering standards, and validation.\nEnable engineers to orchestrate AI-driven workflows with clear testing, review, security, and release-readiness controls.\nBuild a strong technical foundation for sandbox-based execution and parallel agentic development.\nEstablish reusable, governed patterns for AI assistants, retrieval, automation, and internal and customer-facing AI capabilities.\nDemonstrate tangible value through adoption, quality, productivity, delivery, and cost metrics.\nWork Model and Location\nWe are based in the New York metropolitan area and hire across the U.S. East Coast, with working hours aligned to New York time.\nWe value in-person collaboration while recognizing the importance of flexibility and where our employees choose to live.\nHybrid: Candidates located near the New York metropolitan area are expected to work from the office two to three days per week.\nRemote-friendly: Candidates located farther away on the U.S. East Coast can work primarily remotely, with in-person visits approximately four to six times per year.\nOnboarding: Regardless of location, additional in-person collaboration is expected during the initial onboarding period.\nThis role offers the opportunity to shape how AI transforms software engineering practices across product teams while building practical, scalable solutions that deliver measurable results.\nWHY JOIN US\nMatrix is a global, dynamic, and fast-growing technology services and consulting company with over 17,000 employees worldwide. Founded in 2001, Matrix has grown through significant organic expansion and strategic acquisitions, executing some of the largest and most impactful technology projects in the market.\nWe specialize in the design, development, and implementation of advanced technologies, software solutions, and digital products. Our services include infrastructure and consulting, IT outsourcing and offshore delivery, training and assimilation programs, and acting as a trusted implementation and delivery partner for the world’s leading software vendors.\nWith deep expertise across both the private and public sectors-spanning Finance, Telecom, Healthcare, Hi-Tech, Education, Defense, and Security -Matrix serves a strong customer base in Israel alongside a continuously expanding global client portfolio.\nAt Matrix, our people are at the heart of our success. We are a community of talented, creative, and dedicated professionals who are passionate about innovation and excellence. We actively attract, develop, and retain top talent, recognizing that every employee’s contribution is critical to our continued growth and future success.\nMatrix’s success is built on a challenging and engaging work environment, competitive compensation and benefits, and meaningful career development opportunities. We foster a diverse, inclusive culture that encourages collaboration, continuous learning, and growth-together.\nJoin the winning team. Be challenged, grow your career, and enjoy the journey in a highly respected global organization.\nTo learn more, visit: www.matrix-ifs.com\nEQUAL OPPORTUNITY EMPLOYER: Matrix is an Equal Opportunity Employer and Prohibits Discrimination and Harassment of Any Kind. Matrix is committed to the principle of equal employment opportunity for all employees, providing employees with a work environment free of discrimination and harassment. All employment decisions at Matrix are based on business needs, job requirements, and individual qualifications, without regard to race, color, religion or belief, family or parental status, or any other status protected by the laws or regulations in our locations. Matrix will not tolerate discrimination or harassment based on any of these characteristics. 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