Team Lead, Delivery Management
Element451 · Remote, Serbia
About this role
## Team Lead, Delivery Management ### Overview The Team Lead owns what the Bolt delivery team ships across Element451’s AI-powered product experiences. This is a **player-coach** role: you lead and grow the team while staying close enough to the code and product to make real technical calls. You’ll be accountable for **end-to-end delivery**—**velocity, predictability, quality, and safe operation** of customer-facing AI experiences—from planning through **working software live in production**. Bolt brings **AI-powered and agentic experiences** into the workflows colleges and universities use to **recruit, enroll, and support students**. The product shape will evolve as you learn from customers and the market, and this role turns that direction into **dependable product outcomes**. --- ### Key Responsibilities #### Delivery & Execution - Own the Bolt team’s delivery end to end: **velocity, predictability, quality, and production outcomes** (not just merged code or staging). - Shape and plan cycles: size and scope work to a deliverable cycle boundary, ground commitments in honest engineering analysis, and represent team capacity/constraints to Product. - Maintain real-time visibility into capacity, workload, delivery risk, and operational health—**surface concerns early**. - Partner with Bolt Product leadership to translate product outcomes into coherent engineering scope, keeping **cross-team ownership and dependencies explicit**. #### Quality, Standards & Release - Own the team’s **engineering–QA contract**: what a ready handoff looks like and what quality gates must be met. - Reinforce technical standards and architecture owned by Senior/Principal Engineers; hold the team accountable to code quality, review thoroughness, testing, sound architecture, and responsible AI engineering. - Own release health: **“done” means in production and operating as intended**. Drive work to release and remove blockers delaying deployment. - Treat AI quality as an engineering discipline: - Evaluate changes to agent behavior, prompts, models, retrieval/knowledge, tools, and orchestration proportionately to risk. - Use production-informed regression coverage, explicit release gates, progressive rollout, monitoring/rollback, and feedback loops that turn failures into durable test coverage. - Use measured product and operational signals (e.g., correctness, relevance, safety, latency, reliability, adoption, and cost where applicable) to make evidence-based trade-offs. #### Hands-On Technical Engagement - Stay hands-on where it counts (especially while the team is small): tackle technical challenges, review high-risk/high-signal PRs, and join incident response. - Engage substantively in production AI system design/operation (agent workflows, knowledge grounding/retrieval, tool use, human escalation, observability, evaluation, safe rollout). - Escalate architectural and cross-domain decisions beyond the team’s scope to Principal Engineers. - Hold AI-assisted development to the same bar as handwritten work; model disciplined, fully accountable use of AI tooling. #### People Leadership - Lead, coach, and grow a team of **3–8 engineers** (regular 1:1s, actionable feedback, honest career development). - Own hiring (sourcing, evaluation, onboarding) and address underperformance early and constructively. - Build a team that can reason across **deterministic software** and **probabilistic AI** behavior. - Create a culture of excellence, ownership, and psyc
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