Implementation Engineer (Media)

About the Role

The Implementation Engineer is a key bridge between the Business Unit (BU), the extended client organization, and the Core Engineering team. Acting as both advocate and enabler, this role ensures that client needs are understood, validated, and efficiently translated into scalable technical solutions.

Deeply embedded in the Google AI ecosystem, the Implementation Engineer rapidly prototypes, tests, and validates new AI-driven workflows and solutions. Once proven, they collaborate with Core Engineering to productionize and scale them across the broader system. This role sits at the intersection of technology, creativity, and innovation—empowering creative teams to harness AI tools effectively, refine prompts, optimize system instructions, and evolve new agents and features that push the boundaries of what’s possible.

Responsibilities:

• Client & BU Advocacy: Act as the primary liaison between Business Units, clients, and the Core Engineering team to ensure technical deliverables meet user needs.
• Workflow Optimization: Collaborate with creative and operations teams to rework prompts, refine system instructions, and improve AI-driven experiences
• Agent/Agentic Development: Spin up, configure, and maintain new AI agents and Agentic flows within existing tools and environments using existing Google frameworks.
• Feature Translation: Collect and synthesize user and BU feedback into structured feature requests for the Core Engineering team.
• Technical Documentation: Maintain clear documentation on implementations, configurations, and workflows for cross-team transparency.

Key Qualifications

• Strong understanding of AI systems, particularly within the Google AI ecosystem (Vertex AI, Gemini, etc.).
• Hands-on experience leveraging media buying APIs (Meta, Google Ads, DV360, TikTok) to pull performance data, automate bidding and budget updates, and support scalable campaign operations.
• Experience with prompt engineering, grounding, system instruction tuning, and AI agent configuration.
• Familiarity with API integrations, data workflows, and cloud-based architectures (especially Google Cloud).
• Proven ability to prototype quickly using tools such as Python, Apps Script, or no/low-code platforms.
• Strong communication and stakeholder management skills, with experience working between technical and creative teams.
• Ability to document, train, and enable non-technical users effectively.

About You

The essentials:

• 4-6 yrs Engineering
• 1-2 yrs background in applied AI, creative technology, or automation engineering.
• Has worked with Google as a client before 
• Hands-on exposure to LLM orchestration frameworks and AI toolchains.
• Passion for experimentation and a “builder” mindset.

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At Monks, we believe in fostering an environment where a diversity of perspectives can thrive. We proactively work to design hiring processes that promote equity and inclusion while mitigating bias