AI Engineering Specialist

Responsibilities

Key Responsibilities•Build AI and ML pipelines for training, inference, embeddings, retrieval, reranking and grounded generation.•Develop production-grade RAG solutions using appropriate chunking, vector stores, retrieval methods, metadata filters and evaluation datasets.•Design and implement agents using frameworks such as Google ADK and LangChain, including tool use, reasoning flows, memory, caching and multi-agent orchestration.•Integrate agents with internal APIs, search, reporting workflows, enterprise systems and external data sources using MCP or equivalent secure tool-integration patterns.•Implement and run evaluation for accuracy, groundedness, hallucination, bias, tool success, task completion, latency, reliability and cost; add regression tests for prompt, model and workflow changes.•Build and deploy APIs, services and applications on GCP / Vertex AI and, where required, Azure; manage containerization, CI/CD, environment configuration and release readiness.•Monitor models and agents in production, diagnose failures, and optimize token usage, compute, latency, scalability and runtime cost.•Collaborate with the Tech Lead, Product Owner, Data Scientists, Data Engineers and QA teams; document code, APIs, experiments, evaluations and operational runbooks

Qualifications

3-6 years of applied AI / ML engineering experience, with evidence of deploying models, RAG systems, agents or AI services beyond notebooks and prototypes.•Demonstrated ownership of production code, API integration, automated testing, deployment, monitoring and troubleshooting.•Collaborative and agile working style, with the ability to explain technical decisions and AI limitations clearly to media and product teams.•Curiosity, structured problem solving and a strong bias toward reusable, maintainable and measurable solutions.•Media, martech, audience data, research data or automated reporting experience is a strong plus.