Lead Software Engineer, AI (P4577)

SUMMARY

We are seeking a Lead Software Engineer to join our AI Software Development Lifecycle (SDLC) team and build the engineering backbone that scales AI-assisted development across 84.51° and Kroger. As AI workforce tooling matures and adoption grows from pilot teams to dozens of delivery teams, this role provides the systems and patterns that keep pace: deployment automation, standardized templates, and a prompt and skill marketplace that makes best practices discoverable and reusable at scale. This team is charged with re-inventing Software Developer for 84.51º and Kroger with AI at the forefront.
This role provides an opportunity to shape how thousands of developers adopt AI within a large enterprise. Success means turning repeatable friction into platform infrastructure: authoring workflows, creating templates, and contributing plugins to internal marketplaces to shorten onramps and increase AI-assisted development velocity. This role spans the spectrum from hands-on automation engineering to cross-team enablement and requires someone who can both build systems and communicate their value across technical and non-technical audiences.

This is a platform-focused, hands-on engineering role. You will be expected to both provide technical leadership and produce production-quality code, operating across DevOps, Developer Experience, and AI Enablement domains.

RESPONSIBILITIES
Design and build deployment automation for AI workforce tooling — including Agentic Harness configurations, MCP server or similar service-level integrations, and related CI/CD pipelines

Define and enforce standards for AI tooling configuration, security posture, and observability across teams – often via direct enhancements to the coding harness hook and plugin layers

Ensure accurate observability of AI tooling adoption and usage patterns using Datadog, Dynatrace, or related tools

Own and evolve the internal prompt and skill marketplace — enabling teams to publish, discover, version, and reuse prompts, skills, and MCP tools across the enterprise

Develop workflow playbooks that codify AI-SDLC integration patterns for consistent, repeatable adoption across engineering teams

Work directly with delivery teams to embed AI tooling into their development workflows, translating friction and capability gaps into scalable platform improvements

Regularly document and present learnings from platform work and team engagements to the broader AI Enablement organization

Engage with technology, infrastructure, and product teams as necessary to integrate and extend AI workforce tooling

Participate routinely in team on-call rotation and customer support during business hours


MINIMUM QUALIFICATIONS, SKILLS, AND EXPERIENCE
4-year degree in a technology related discipline (Computer Science, Computer Engineering, Information Systems, etc.) or equivalent work experience

Minimum 5+ years of experience producing high-quality software on Agile development teams (Python preferred)

5+ years of experience with public cloud technologies (Azure preferred)

3+ years of experience in Platform Engineering, DevEx, or related roles

Demonstrated experience building or operating CI/CD pipelines and deployment automation (GitHub Actions or equivalent)

Familiarity with AI-related concepts such as LLMs, Agents, and Model Context Protocol (MCP)

Strong experience with AI coding tools like Claude Code, Codex, Cursor or equivalent.

Strong interpersonal and communication skills with demonstrated experience working across technical teams and non-technical business units

Desire to learn new technology and grow across different areas of technology


PREFERRED QUALIFICATIONS, SKILLS, AND EXPERIENCE
Experience building or operating internal developer platforms, marketplaces, or catalog systems

Experience developing agentic AI applications, prompt libraries, skill registries, or MCP servers

Experience working with IaC systems, e.g. Terraform/Spacelift, Kubernetes, Helm

Strong technical writing, documentation, or playbook-authoring skills

Demonstrated ability to prioritize own workload with multiple responsibilities and adaptability to changes in those priorities

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