Lead AI Enablement Engineer

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