{"id":730754,"url":"https://alion.io/job/electric-mind-ai-enablement-senior-engineer","title":"AI Enablement Senior Engineer","company":{"id":58136,"name":"Electric Mind","domain":"electricmind.com","url":"https://alion.io/company/electric-mind","size_band":"51-200","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Lever","truth_index":{"grade":"B","score":81,"open_postings":4,"ghost_share":0,"stale_share":0.75,"repost_share":0,"time_to_fill_p50_days":38,"computed_at":"2026-11-03T05:54:40Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","employment_type":"contractor","work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Toronto, Canada"],"countries":["CA"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":89000,"max_usd":164000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":74},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Confluence","optional":false},{"name":"Human-in-the-Loop","optional":false},{"name":"Jira","optional":false},{"name":"Model Context Protocol","optional":false},{"name":"Prompt Engineering","optional":false}],"status":"live","first_seen_at":"2026-08-04T20:31:04Z","employer_posted_date":"2026-08-04","last_verified_at":"2026-11-03T13:41:06Z","board_verified":true,"closed_at":null,"days_open":90,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":90},"description":"About the Role\nSupport the AI Enablement Technical Lead in the delivery and coaching of the AI adoption across multiple client teams. The Senior Engineer mentors technical client team members (Developers, Quality Engineers, ...) on the optimal use of tools and techniques involving AI that can be used to enable the software delivery lifecycle. This will include but not be limited the use of skills, agents, and prompt engineering techniques to improve AI interactions, the u application of those techniques to support the generation of code, tests, review PRs and other technical activities conducted as part of the AI-enabled delivery lifecycle.\nAI Enablement Senior Engineer will be reporting into the AI Enablement Technical Lead and escalate any issues or findings outside of their remit, that impede or could impede AI adoption to the AI Enablement Technical Lead.\nWhat You’ll Do\nAct as the contact person for coaching and questions related to AI adoption and AI enablement in the software delivery lifecycle, from technical client team members which the team cannot resolve themselves through the training they will have been provided.. This responsibility will cover multiple client teams. The Senior Engineer must be able to:Mentor technical client team member on how to validate plans and code generated by AI, against intended story outcomes .\nCoach client team members on focusing on delivery rather than detouring into AI skills changes mid-flight capturing friction for later skill change iterations.\nSupport client team members in instilling a strong review ethic across code, tests, PRs and other AI generated artifacts requiring HITL (Human In The Loop) reviews. Guide the teams in focusing reviews on content and alignment with acceptance criteria rather than style.\nHelp diagnose experiences and environment friction in real usage (skill discovery, workspace confusion, command generation, dependencies, tests, branch and PR flow, IDE/tool timeouts, request limits, CLI setup). Treat environment friction as separate from AI-method friction - surface anything being worked around rather than fixed, and either help fix it or escalate it so it is not silently absorbed as “AI doesn’t work.”\nSupport the integration of tooling (scripting, MCP, ...) by client teams needed by client teams to facilitate the automation of activities between AI and existing enterprise solutions (e.g. Jira & Confluence integrations).\nSupport the creation of the AI knowledge base creation and related AI supported reverse-engineering activities as required by various client teams.\nSupport the setup of the AI enabled project environment (repositories, skills, agents, configuration files, etc) as needed by each client solution’s or client team’s specific requirements and context.\nCoach client team technical staff (Developers, Quality Engineers, ...) how to validate that AI-generated output respects each team’s engineering constraints, coding guidelines, test requirements, enterprise standards, etc.\nWork with the indicated team members to capture and improve AI skills and agentic capabilities, reinforcing that skills encode team knowledge .\nEscalate to the AI Enablement Technical Lead when technical decisions, tooling blockers, access, or capacity issues fall outside the role’s remit, and surface recurring friction across your assigned client teams rather than solving these in isolation.\n\nKey Deliverables\nGuidance and mentoring for technical client team members (Developers and Quality Engineers mainly)\nSupport deep-dive sessions and story walkthroughs across its assigned client teams whereas these relate to the ability for team to further AI enablement.\nCreation and supporting the creation of guidance for technical personal such as walkthrough notes, step by step job aides etc..\nCoaching in AI enabling techniques and practices as per the program’s standards and approach.\nLogging and sharing/escalating experience and environment/tooling-friction findings.\nSupport for AI Knowledge-base creation and reverse-engineering through in person assistance or the delivery of guidance artifacts (documentation, job aids, ...).\nCapture of AI skill improvement requirements across client teams, and collaboration with program team to elicit these improvements.\nScripting or support of scripting for enterprise tool integration (e.g. Jira, Confluence, ...).\nDeveloper-friction pattern reporting across its assigned client teams.\nWhat You’ll Bring\nStrong software delivery experience across various technology stacks using industry best practices (versioning, PR reviews, solution design, code, ...).\nUnderstanding of how to apply AI capabilities such as Spec Driven development to support AI enablement of the Software Delivery Lifecycle.\nAbility to run practical, hands-on enablement sessions with technical personnel, and support client team members in running the same.\nFamiliarity with AI-assisted coding tools, agentic IDE workflows, skills, model behaviour, and common AI failure modes.\nAbility to diagnose friction across setup, workspace structure, command execution, dependencies, tests, and source-control flows, and to distinguish environment friction from AI friction.\nWorking knowledge of AI-assisted Quality Engineering methods(e.g. test generation, execution, defect review) .\nGood communication skills and the ability to coach technical personnel who may be skeptical of AI-generated output or attached to existing coding habits.\nEnough architectural and code literacy to distinguish an AI skill/prompting problem from a specification quality, context, setup, or codebase problem.\nFamiliarity with SpecDriven development solutions is a plus.\nAbility to help pods test and validate the story-development / quick-dev skill chain against realistic stories, including UI framework, API, and other story flavours as they become available.\nWorking knowledge of the A.I.D.E Framework.\nComfort using AI tools to complete job 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