AI/Automation Development Lead

dentsu•New York, NY

1d•$139,289 - $165,000•Hybrid

About The Position

The AI/Automation Development Lead is the hands-on technical leader for a small, high-output development team building AI-native applications and agentic automation systems. This is a coding-first role: you will write production code daily, lead by example in the codebase, and set the technical standard for a growing engineering function. The defining characteristic of this role is that AI is not a feature you bolt on — it is how you build. You develop with Claude Code or equivalent AI-assisted tools as your default workflow. You design and implement agentic AI systems — autonomous agents, skills, tool-use patterns, and multi-step automation workflows — as core deliverables, not side projects. The stack (Flask/Python, React, Azure, MySQL) is the toolbox; agentic AI development is the craft. You will take architectural direction and scoped requirements from the Engineering Manager, then own the technical execution: building, shipping, reviewing code, and driving the team to deliver. Approximately 60–70% of your time is hands-on development; 30–40% is technical leadership, code review, and team coordination.

Requirements

5+ years of full-stack development experience with Python/Flask and React or equivalent modern frameworks
Practical experience designing and building agentic AI workflows — autonomous agents, skills, tool-use patterns, and multi-step automation using LLM platforms (Claude, OpenAI, or equivalent)
Proficiency with AI-assisted development tools (Claude Code, Cursor, or similar) as a daily working practice, not occasional experimentation
Strong experience with Azure cloud services, particularly Azure Container Apps, Key Vault, and containerized deployments
Expert-level SQL and hands-on experience with relational database administration, schema design, and performance optimization (MySQL preferred, Postgres or equivalent acceptable)
Solid Git workflow management: branch protection policies, PR-based promotion flows, merge conflict resolution, and the discipline to enforce them across a team
Working knowledge of CI/CD deployment workflows in Azure DevOps or GitHub Actions — you won’t own pipeline development, but you need to work fluently within them
Experience with ETL pipeline development using Azure Data Factory or equivalent platforms
API development and integration experience (REST, webhooks, OAuth/SCIM flows)
Experience leading or mentoring a small development team, including distributed/offshore team members
Strong code review discipline and ability to raise the technical bar for others
Comfort working within structured governance and approval processes
Clear communication skills — able to explain technical decisions to non-technical stakeholders and translate engineering constraints into plain language
Experience with Agile/Scrum practices and comfort with iterative delivery
Engineering fundamentals first: you write clean, maintainable code and expect the same from your team, whether the code was AI-assisted or hand-written
Ownership mentality — you see a broken pipeline, a drifting schema, or a missed test, and you fix it or flag it, not route around it
Genuine curiosity about emerging AI capabilities and a drive to put them into production, not just experiment
Comfort with ambiguity in a team that is scaling and defining its processes as it grows

Nice To Haves

Experience with enterprise IAM systems (Okta, SCIM provisioning, Entra ID)
Familiarity with containerized application deployment and Docker
Azure or cloud platform certifications (Azure Developer Associate, Azure DevOps Engineer, or equivalent)
AI platform certifications (OpenAI, Copilot, AWS AI/ML)
Experience with data modeling techniques (Star Schema, dimensional modeling) and enterprise analytics
Track record delivering compliance-sensitive applications in regulated or enterprise environments
Knowledge of DevOps and DataOps practices, infrastructure-as-code, and configuration management

Responsibilities

Design and build agentic AI solutions as a primary focus: autonomous agents, skills, tool-use patterns, and multi-step automation using LLM platforms (Claude, OpenAI, Copilot)
Develop AI-powered applications that integrate agentic capabilities into enterprise workflows — replacing manual processes with intelligent automation
Build full-stack applications using Flask/Python backends and React frontends, deploying to Azure Container Apps
Develop with Claude Code or equivalent AI-assisted tools as your default workflow — AI-assisted development is the baseline expectation, not a nice-to-have
Design and maintain MySQL database schemas, write performant SQL, and build ETL pipelines for enterprise data workflows
Implement end-to-end solutions incorporating data pipelines, automation workflows, and AI capabilities across multiple active codebases
Work within established CI/CD pipelines in Azure DevOps, enforce branch protection policies, and own Git workflow discipline across the development team
Provide technical direction and hands-on code review for a combined team of onshore and offshore developers
Establish and enforce development standards: branching strategy, PR-based workflows, test coverage expectations, and deployment procedures
Drive adoption of AI-assisted development practices across the team as a practical approach to accelerating delivery
Conduct regular code reviews and pair programming sessions to raise the team’s technical bar
Facilitate knowledge sharing and cross-training across time zones to reduce single-point-of-failure risks
Participate in hiring and onboarding as the engineering function grows
Take architectural direction and scoped requirements from the Engineering Manager and drive them through to production-ready code
Own delivery timelines for development workstreams, balancing speed with sustainability — the ability to say “yes, and here’s what it’ll take” is valued over unconditional commitment
Build solutions that work within established governance frameworks (Legal, Security, CIO approval chains) without treating compliance as an obstacle
Ensure applications meet compliance requirements (ICF/JSOX, data privacy regulations) by design through code quality and testing discipline
Participate in discovery sessions and requirements gathering alongside the Engineering Manager for complex projects