Director, AI Engineer

Job Description: Role Summary   Lead dentsu's applied AI engineering team for the Data Experience practice. You will own the full stack of what we ship: web applications, the agentic harness underneath them, and model hosting and observability on Databricks. You will manage a senior India-based team of AI developers and full-stack engineers, set the engineering standard, and stay hands-on in the code. This is a builder-leader role: you design supervisor graphs, review pull requests, tune context and token budgets, and ship agentic products that run in production, not demos.  Key Responsibilities   Lead and develop a senior India-based AI engineering team — two Directors and three Senior Managers (AI developers), two full-stack engineers, and a front-end developer; set priorities, run delivery across time zones, and grow the bench  Build full-stack web applications end to end — Python services, APIs, and modern front ends that put agentic capability in front of internal teams and clients  Engineer the agentic harness — the runtime layer that manages tool calling, state, memory, retries, guardrails, human-in-the-loop checkpoints, and evaluation for every agent we ship  Design multi-agent systems in LangGraph — supervisor node structures, sub-agent routing, agent-to-agent (A2A) communication, and shared state across long-running workflows  Ship agentic features inside web apps — embedded assistants, tool-using workflows, and streaming UX that feel native to the product rather than bolted on  Own context engineering — what goes into the context window, when, and why: retrieval, summarization and compaction, tool-result shaping, and memory strategy  Drive token management and optimization — prompt caching, model routing by task, context budgets, and per-feature cost and latency tracking  Host and operate models and agents on Databricks — Model Serving, Unity Catalog governance, and MLflow 3 tracing and evaluation for end-to-end observability  Set the AI-native development standard — Claude Code, Cursor, and Codex as daily tools; codify rules, prompts, and review practices so the team ships faster without sacrificing quality  Run engineering hygiene through GitHub — branching strategy, code review, CI/CD with GitHub Actions, automated testing, and environment promotion from dev to production  Partner with product, data engineering, and client teams — translate business problems into agent designs and explain trade-offs to non-technical stakeholders  Required Qualifications   8+ years in software engineering, including 2+ years building LLM-powered or agentic systems in production, with experience leading senior engineers  Strong hands-on experience with the Claude Agent SDK or the OpenAI Agents SDK (or equivalent) — building and shipping agents, not just calling a completions endpoint  Proven experience engineering agentic harnesses — tool orchestration, state management, error recovery, guardrails, and evaluation  Deep context engineering skills, including token management and optimization and prompt caching strategies  Expert Python and strong LangGraph experience, including supervisor and multi-agent patterns and agent-to-agent communication  Comfortable building full-stack web applications — Python backends (FastAPI or similar), REST and streaming APIs, and a modern JavaScript front end (React / Next.js or similar)  Experience deploying models and agents on Databricks with MLflow, including MLflow 3 tracing and evaluation for observability  Fluent with AI coding tools — Claude Code, Cursor, or Codex as part of your daily workflow, and able to teach others to use them well Solid DevOps fundamentals on GitHub — CI/CD pipelines, GitHub Actions, code review, testing, and secrets and environment management  Track record managing distributed or offshore engineering teams across time zones  Preferred Qualifications   Data engineering background — Spark / PySpark, lakehouse architecture, SQL, and working with governed gold data products  Experience building Model Context Protocol (MCP) servers or implementing the A2A protocol  LLM evaluation practice — offline eval sets, LLM-as-judge, and regression testing for prompts and agents  Experience with Databricks Apps and Lakebase (Postgres)  Experience in media, advertising technology, or marketing analytics environments Additional Information The annual salary range for this position is $94,000 - $170,000. Placement within the salary range is based on a variety of factors, including relevant experience, knowledge, skills, and other factors permitted by law. #LI-Hybrid #LI-MS3 Benefits available with this position include: • Medical, vision, and dental insurance, • Life insurance, • Short-term and long-term disability insurance, • 401k, • Flexible paid time off, • At least 15 paid holidays per year, • Paid sick and safe leave, and • Paid parental leave. Dentsu also complies with applicable state and local laws regarding employee leave benefits, including, but not limited to providing time off pursuant to the Colorado Healthy Families and Workplaces Act, in accordance with its plans and policies. For further details regarding Dentsu benefits, please visit www.dentsubenefitsplus.com. At dentsu, we believe great work happens when we’re connected. Our way of working combines flexibility with in-person collaboration to spark ideas and strengthen our teams. Employees who live within a commutable distance of one of our hub offices, currently located in Chicago, metro Detroit, Los Angeles, and New York City, are required and expected to work from the office three days per week (two days per week for employees based in Los Angeles). Dentsu may designate other Hub offices at any time. Those who live outside a commutable range may be designated as remote, depending on the role and business needs. Regardless of your work location, we expect our employees to be flexible to meet t