Senior AI Platform Engineer — Generative AI Systems

lgads; Denver (NY); On-site; USD 162,000 - 231,000; Full time

Benefits offered by this job

100% employer-paid medical, dental, &-

Company-paid life and AD&D

401(k) with company match

Flexible Time Off

Paid parental leave

Job summary

LG Ads is seeking an AI Platform Engineer to help build the in-house platform powering generative AI agents deployed business wide. You’ll contribute to the end-to-end stack, including LLM serving, RAG pipelines, and production APIs, with a focus on latency, throughput, and cost.

The role is hands-on, writing production Python and integrating with AWS Bedrock and vector stores. You’ll work in an agile environment, collaborating with cross-functional teams to design scalable AI infrastructure and

Qualifications

• Proven experience designing and developing Generative AI agents.
• Strong expertise in intent detection and complex query decomposition.
• Demonstrated experience with context engineering for AI models.
• Understanding of knowledge graphs and vectorization for LLMs.
• Experience building RAG systems end-to-end.
• Solid understanding of LLMs and enterprise applications (e.g., Llama 4 Maverick, Claude, OpenAI).
• Experience with AWS for scalable compute and storage.

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.

Skills

Generative AI agents

Intent detection

Context engineering

Knowledge graphs / vectorization

RAG systems

LLMs in enterprise

AWS cloud

Databricks / Snowflake

Agile development

Education

Bachelor's or Master's in CS/AI/ML

Tools

AWS

Databricks

Snowflake

Job description

LG Ads is seeking an AI Platform Engineer to help build the in-house platform powering generative AI agents deployed business wide. You’ll contribute to the end-to-end stack, including LLM serving, RAG pipelines, and production APIs, with a focus on latency, throughput, and cost.

The role is hands-on, writing production Python and integrating with AWS Bedrock and vector stores. You’ll work in an agile environment, collaborating with cross-functional teams to design scalable AI infrastructure and