FulcrumAbout Fulcrum: We’re a team of tech-savvy, creative & passionate IT professionals. We’ve created a vibrant and performance-driven culture for ourselves where everyone is free to think & act outside the box. There are literally no limits to what you can do here, as long as we WOW our clients and OVER deliver what we promise.
We are now looking for an Engineering manager to join our team Core mission of the role is to be the quality and process backbone for all Product Tech teams at Fulcrum. Ensure every team follows best practices, delivers on schedule, adopts AI-powered workflows, and maintains engineering excellence — without micromanaging day-to-day execution.
Key Responsibilities Delivery & process control monitor overall health of AI projects (velocity, quality, blockers, timelines) and intervene early when needed review and approve ключові технічні deliverables (architecture, APIs, security, DB, deployment)
define and enforce engineering processes (code reviews, CI/CD, testing, documentation, AI tooling)
approve presale development plans (architecture, stack, team, estimates)
approve discovery setup (processes, cadence, risks, tooling, acceptance criteria)
establish and maintain cross-project engineering standards
Ai process & innovation drive adoption of AI dev tools and measure impact (Claude, Codex, agents)
evaluate new AI requirements (feasibility, effort, risks, approach)
identify and внедряти AI-driven process improvements (automation, testing, monitoring, docs)
oversee custom agent development (quality, value, best practices)
track and evaluate new AI/ML technologies, maintain internal knowledge base
People & education lead internal AI education (workshops, materials, onboarding)
support engineer growth via feedback and skill gap identification participate in hiring and technical assessments for AI roles run mentorship initiatives for AI skill development Engineering & delivery
2+ years managing engineering teams or overseeing multi-project delivery
5+ years in IT (any relevant roles)
proven track record of shipping production systems on time and within scope
AI / ml expertise hands-on experience with AI/ML projects (LLMs, agents, vector DBs, prompt engineering)
understanding of agentic systems, model behavior, and AI-specific risks
Architecture & process ability to review and challenge architecture, tech stack, APIs, DB, and security decisions strong experience defining and enforcing engineering processes (CI/CD, code reviews, testing, documentation)
Collaboration & communication strong cross-team communication (CTO, POs, designers, engineers)
ability to translate technical decisions into business impact People & leadership - experience mentoring engineers, running knowledge sharing, and supporting hiring AI-powered workflows - hands-on use of AI dev tools (Claude, Codex, Copilot, etc.) and understanding of productivity impact Presale & discovery - experience validating technical feasibility, estimates, and discovery setup Quality & security understanding of testing strategies (unit, integration, E2E, AI validation)
awareness of security, compliance, and responsible AI practices Tech stack awareness - solid understanding of modern stack (TypeScript, React, Node, Docker, PostgreSQL) with ability to review code
Nice to have experience building or orchestrating AI agents / multi-agent systems familiarity with vector DBs, RAG pipelines, or open-source AI frameworks
DevOps / platform engineering experience (CI/CD, monitoring, infra)
experience in outsourcing/agency environments strong product sense and technical documentation skills
KPI & Success metrics Delivery & quality
90%+ on-time delivery across AI projects
30% reduction in post-release defects all PRs reviewed within 24h, zero critical issues from unreviewed code
Process & compliance
100% adherence to defined engineering processes stable sprint velocity and predictable delivery
AI adoption & efficiency
80%+ of engineers actively using AI dev tools measurable productivity improvements from AI workflows
Presale accuracy estimates within ±15% of actual delivery timelines
Knowledge & enablement regular knowledge-sharing workshops (2+/month) + actual AI handbook
Team health engineering NPS ≥ 8 (process, support, enablement)
Hiring process Recruiter interview (40 min)
Final interview with CTO What we offer:
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