We are seeking a highly skilled AI Deployment Lead – Devin AI with deep expertise in Devin and modern Generative AI (GenAI) tools to support enterprise-scale rollout and adoption across Line of Business (LOB) engineering teams.
This is a hands-on engineering role focused on enabling LOB teams to improve their product delivery lifecycle through effective utilization of GenAI tools, coupled with a strong ability to train and upskill engineering teams on autonomous development workflows. The ideal candidate combines solid software engineering fundamentals, practical experience integrating GenAI solutions into enterprise development environments, and demonstrated success teaching others to be effective with these tools — not just executing tasks independently.
This role requires close partnership with product, platform, security, and engineering teams to accelerate delivery, improve developer productivity, and drive scalable, well-governed adoption of GenAI capabilities across the organization.
Responsibilities
- Lead the end-to-end rollout and scaling of GenAI tools (Devin, Claude Code, Cursor) across CDXO and LOB engineering teams.
- Act as a hands-on engineer, building and integrating solutions into enterprise SDLC workflows (CI/CD pipelines, repositories, APIs).
- Define and execute enterprise-wide rollout strategies, including phased onboarding and scaling models.
- Partner with LOB teams to enhance the product delivery lifecycle using GenAI capabilities.
- Design and deliver structured developer onboarding, training, and enablement programs — teaching engineering teams to work effectively with autonomous development tools, not simply completing tasks on their behalf.
- Advise teams on when to apply autonomous (Devin) vs. assistive (Copilot) approaches based on use case and risk profile.
- Monitor tool adoption, usage metrics, and performance, ensuring continuous optimization.
- Provide hands-on troubleshooting, performance tuning, and scaling support.
- Establish reusable playbooks and best practices for enterprise-wide GenAI adoption.
- Ensure alignment with enterprise governance, security, and compliance frameworks, including secrets/identity management practices for agent-based tooling.
- Identify adoption gaps and proactively drive improvements in efficiency and scalability.
Requirements
Experience
- 5–8+ years of software engineering experience, with strong fundamentals in at least one enterprise stack (e.g., Java, Python, or TypeScript/React/Angular).
- Working knowledge of distributed systems, microservices, event-driven architectures, and API design/integration.
- Hands-on experience with CI/CD pipelines and developer tooling ecosystems.
- Familiarity with cloud platforms such as OpenShift (OCP) and Google Cloud Platform (Google Cloud Platform).
- Demonstrated experience training, mentoring, or enabling engineering teams on new tools or workflows — prior instructor, enablement, or forward-deployed engineering experience strongly preferred.
- GenAI / LLM Expertise
Hands-on experience with:
- Devin
- Claude / Anthropic models
- Cursor or similar developer productivity tools (Copilot, etc.)
- Strong understanding of:
- Prompt engineering
- RAG (Retrieval Augmented Generation)
- Agent-based workflows and tool usage
- Multi-agent architectures
- MCP (Model Context Protocol) servers and integrations
- Agentic systems including orchestration, tool chaining, and autonomous decision-driven workflows
- Governance & Security
- Familiarity with enterprise AI governance and responsible-AI frameworks in regulated environments.
- Working knowledge of secrets management and identity platforms (e.g., CyberArk, HashiCorp Vault) and enterprise IAM/SSO (e.g., Entra ID) as applied to agent identity and access control is a plus.
Preferred
Ability to thrive in fast-paced, evolving environments.
Strong collaboration skills with cross-functional and LOB teams.
Capability to translate business requirements into technical solutions.
Experience working in enterprise environments with governance & compliance requirements.
Strong passion for driving adoption and improving developer productivity — with the patience and communication skills to teach, not just build.
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