Lead AI Enablement Engineer (AI-First DevEx / Agentic Workflows)

Lead AI Enablement Engineer OnTarget Labs is a leading international software product development company. We create next generation of world class product lines. The company is looking for a Lead AI Enablement Engineer, AI-First DevEx to join our innovative product team as a full-time member working REMOTELY . Lots of opportunities for professional growth and business trips abroad are offered. Join our friendly team of IT professionals now! Product description A fast-growing B2B SaaS platform in the US property management industry. Our platform supports complex multi-tenant workflows, integrations, payments, and operational processes that real businesses rely on every day. As we grow, we are investing in AI-first engineering, developer experience, and modern engineering foundations so product teams can ship faster, safer, and with more confidence. We are looking for a hands-on AI enablement engineer to make AI-assisted delivery practical, repeatable, trusted, and tied to real engineering outcomes. The Role This is a hands-on IC role inside the Platform team, with ownership for leading the AI enablement initiative across product engineering teams. You will partner closely with the Platform Lead to scale and improve AI-assisted workflows across the software delivery lifecycle. The role will help move these workflows from mostly local usage into more structured, validated, and eventually sandbox-based agent orchestration, while also shaping reusable AI patterns for product-facing capabilities and internal employee agents. What You Will Do Establish practical AI-first workflows for coding, debugging, testing, planning, design, docs, reviews, and production investigation. Build reusable agent instructions, prompt patterns, skills, repo context, integrations, and examples that teams can apply directly. Partner with the Platform Lead to connect AI workflows with CI/CD, sandboxes, parallel agent workflows, testing, observability, release readiness, and DX signals. Create validation loops so AI output can be checked through tests, reviews, quality signals, security controls, and release checks. Coach engineers through demos, pairing, docs, examples, office hours, and practical workflow design. Shape reusable patterns for AI-powered product and internal capabilities, including assistants, retrieval, automation, governance, access, and reuse. Use adoption, quality, productivity, cost, and delivery signals to prioritize work and measure real leverage. What You Bring Excellent verbal and written communication skills in English 7+ years of software engineering experience preferred, with strong senior-level candidates considered; experience improving DevEx, platform capabilities, internal tooling, engineering productivity, or AI enablement is especially relevant. Deep hands-on use of AI tools for real engineering work, especially Claude Code, Warp, AI agents, LLM APIs, MCP workflows, or similar tools. Strong engineering depth, with experience building reusable workflows, integrations, reference implementations, prompts, agent skills, evaluation patterns, and automation. Practical understanding of planning, implementation, testing, reviews, CI/CD, release readiness, production support, and documentation. Strong judgment around AI output quality, validation, testing, reviewability, security, privacy, cost, and operational risk. Ability to drive adoption through coaching, examples, documentation, pairing, workshops, office hours, or enablement programs. Practical AI-first mindset focused on measurable engineering outcomes. Bachelor’s degree in Information Systems, Computer Science, or a related field Technology and Tools Strong candidates will have hands-on experience with several of the following: Claude Code, Warp, AI coding agents, LLM-based developer workflows, or similar tools. Agent SDKs/APIs from Anthropic, OpenAI, AWS Bedrock, or similar platforms. MCP servers, tool calling, agent workflows, reusable instructions, prompt patterns, skills, or orchestration. Depth in at least one modern engineering language, such as Python, TypeScript, Go, C#, .NET, or similar. RAG, semantic search, knowledge agents, embeddings, vector databases, or retrieval patterns. Git workflows, CI/CD, test automation, code review, release readiness, and developer productivity tooling. AWS, cloud environments, containers, or sandbox-style execution environments. Observability, evaluation, monitoring, logging, tracing, cost tracking, and quality measurement. Security, access control, privacy, auditability, and governance for internal and product-facing AI systems. Nice to Have Sandboxed or cloud-based agent execution, parallel AI workflows, or human-as-orchestrator patterns. AI-powered SaaS features, internal knowledge-base agents, Slack agents, or employee AI assistants. Legacy modernization in a mature production SaaS environment. Fintech, payments, accounting, eCommerce, property management, or other transactional domains. Security, compliance-aware delivery, data access controls, or regulated environments. What Success Looks Like Product engineering teams have practical AI-first paved roads for common development workflows. AI-assisted engineering workflows create measurable leverage across product teams, improving delivery speed, quality, review confidence, and developer experience. AI workflows better reflect product context, business rules, codebase structure, and engineering standards. Engineers can orchestrate AI-driven work with clearer validation, testing, review, and release-readiness loops. Platform and AI Enablement create stronger foundations for sandbox-based and parallel agentic delivery. Product and internal teams have shared patterns and guardrails for AI assistants, retrieval, automation, and governed reuse. We offer Competitive compensation to be defined upon the interview results Full time REMOTE WORK