We require people to be on-site, 4 days/week at our Denver or NYC office and are unable to offer relocation support.Job Summary:LG Ads is seeking a motivated AI Platform Engineer to join our AI team and help build the in-house platform powering generative AI agents deployed business wide. You'll contribute to the end to end stack; LLM serving and inference, RAG pipelines, evaluation harnesses and the APIs and infrastructure that put agents in production driving efficiency, unlocking new revenue streams and enabling self-service data capabilities across the organization. The role is hands-on: you'll write production Python, integrate with services like AWS Bedrock and vector stores, instrument systems for observability and tune for latency, throughput and cost. We're looking for someone with a foundation in platform engineering and a working understanding of intent detection, query decomposition and context engineering, who is comfortable with agile delivery and can thrive in an ambiguous, fast-paced environment.
Responsibilities:- Build and maintain LLM-powered services and APIs (FastAPI, webhooks, LangGraph), translating prototypes into production-ready endpoints with proper error handling, retries and timeouts.
- Develop evaluation harnesses and offline/online eval pipelines to measure quality regressions, hallucination rates and task specific accuracy as models and prompts evolve.
- Instrument services with logging, tracing, and metrics (latency percentiles, token usage, error rates) so production behavior is observable and debuggable.
- Design and develop intelligent AI agents capable of intent recognition and decomposing complex queries into smaller, executable tasks that run in sequence or parallel.
- Implement and optimize context engineering techniques to ensure agents leverage relevant short and long-term memory, as well as our aggregated knowledge base, for accurate and insightful responses.
- Integrate AI agents with internal systems such as ACR, Mosaic, and Salesforce, and third-party services like SpringServe and DSPs.
- Utilize and contribute to the development of standardized tooling protocols to streamline integration and maintenance of AI agents.
- Collaborate with cross-functional teams, including product, and business units, to identify and build AI solutions that span the entire company.
- Develop and implement solutions for operational efficiencies, such as automating media planning and integrating agents into Mosaic (Home Grown DSP).
- Build client-facing tools for voice and natural language data queries, supporting custom data and contributing to data monetization efforts for the ACR platform.
- Automate repetitive tasks in General & Administrative (G&A) functions, starting with Finance, and expanding to HR and IT Operations.
- Enable self-service data access and analysis using AI agents, supporting diverse data sources.
- Participate in agile development sprints, actively contributing to planning, execution, and review.
- Manage ambiguity and adapt to evolving requirements in a rapidly developing AI landscape.
Required Skills and Qualifications:- Proven experience in designing and developing Generative AI agents.
- Strong expertise in intent detection and complex query decomposition.
- Demonstrated experience with context engineering for AI models.
- Proven understanding of knowledge graphs and/or vectorization as it relates to LLMs
- Experience building RAG systems including low level design and implementation of such systems.
- Solid understanding of large language models (LLMs) and their application in enterprise solutions, including foundational models like Llama 4 Maverick, Claude, and OpenAI.
- Experience with cloud platforms, particularly AWS, for scalable compute and storage resources.
- Familiarity with Databricks for processing structured and unstructured data, and its use as a runtime for agents and MCPs.
- Experience working in an agile development environment.
- Experience in Databricks / Snowflake and all the associated AI components
- Experience in AWS ( or the two other big clouds) and the associated AI Services
Education:- Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, or a related quantitative field.
