Software Engineer

We require people to be on-site, 4 days/week at our Denver or NYC office and are unable to offer relocation support.

About LG Ads:

LG Ads is a leading global ad solutions company, transforming the advertising landscape through innovative technologies. We are building the future of AdTech with our A.I. Data System (ADS), our proprietary AI agent designed to enhance every aspect of our business, from internal operations to external partner experiences.

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.