LG Ads
Technology
AIEngineer,GenerativeAIAgents
Neural analysis suggests this role is
optimal for Mid+ candidates.
“AI Engineer, Generative AI Agents at LG Ads. Skills: Generative AI, AI agents, LLMs, Platform engineering. Build LLM-powered services. Build LLM-powered APIs”
What You'll Achieve.
Enhance business operations; Enhance partner experiences; Drive efficiency; Unlock new revenue streams; Enable self-service data capabilities; Measure quality regressions; Measure hallucination rates; Measure task specific accuracy; Ensure agents leverage relevant memory; Ensure agents leverage knowledge base; Provide accurate responses; Provide insightful responses; Streamline integration; Streamline maintenance; Build AI solutions company wide; Support custom data; Support data monetization; Automate G&A functions; Expand to HR; Expand to IT Operations; Support diverse data sources
Industry & Context.
Troubleshooting; Root cause analysis
4 days/week onsite, No relocation support
What They're Looking For.
Must Have
Proven experience designing Generative AI agents, Expertise in intent detection, Expertise in complex query decomposition, Demonstrated experience context engineering, Proven understanding knowledge graphs, Proven understanding vectorization LLMs, Experience building RAG systems, Solid understanding LLMs, Experience with cloud platforms, Experience working agile development, Experience Databricks / Snowflake, Experience AWS
Nice to Have
Master's degree in Computer Science, Master's degree in Artificial Intelligence, Master's degree in Machine Learning, Experience with AWS Bedrock, Experience with Databricks for MCPs, Experience with other big clouds
What You'll Do.
Build LLM-powered services
Build LLM-powered APIs
Develop evaluation harnesses
Develop offline eval pipelines
Develop online eval pipelines
Instrument services with logging
Instrument services with tracing
Instrument services with metrics
Implement context engineering
Optimize context engineering
Integrate AI agents internal systems
Integrate AI agents third-party services
Contribute to standardized tooling
Develop solutions operational efficiencies
Automate media planning
Integrate agents into Mosaic
Build client-facing tools
Contribute data monetization
Automate repetitive tasks
Enable self-service data access
Enable self-service data analysis
Participate agile development sprints
Adapt evolving requirements
How You'll Work.
Team & Collaboration
Cross-functional teams; Product teams; Business units; Agile development sprints
Process & Methodology
Agile delivery, Agile development sprints
Full Job Description
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 compl
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