Gen AI Project Manager responsible for designing, developing, and deploying Custom AI agents using LLMs and multi-modal models, and managing complex generative AI programs.
Responsibilities
Demonstrate advanced programming expertise in Python with TensorFlow, PyTorch, and Hugging Face Transformers
Architect and implement Retrieval-Augmented Generation (RAG) pipelines
Design, develop, and deploy Custom AI agents using LLMs and multi-modal models
Implement and manipulate complex algorithms for generative AI models
Manage data pipelines including pre-processing, augmentation, and synthetic data generation
Lead research at the intersection of industry-scale data and academic rigor in Document Understanding, designing and implementing AI-native agentic systems to unlock insights from billions of historical and genealogical records.
Responsibilities
Innovate with State-of-the-Art AI by implementing cutting-edge AI solutions for key Document Understanding tasks such as OCR/HTR, transcription, Named Entity Recognition, Relation Extraction, Coreference Resolution, Summarization, and Knowledge Graphs
Architect Agentic Systems by designing and implementing multi-agent workflows using frameworks like LangChain, LangGraph, CrewAI, AutoGen, AgentCore, Strands, Google ADK, A2A
Analyze and Optimize Multi-Modal Models by evaluating performance of multi-modal AI & LLMs such as GPT, Gemini, Claude, Llama, and Qwen for zero-shot and few-shot scenarios
Senior AI Engineer role is a subject matter expert in applied AI and ML systems including generative and agentic technologies at Alaska Airlines and Horizon Air.
Compensation
$117,700-$176,550 / year
Responsibilities
Build and ship AI/GenAI enabled solutions that deliver measurable business value, from rapid full-stack prototypes that validate business demand to robust production systems that automate workflows and drive operational efficiency.
Design and optimize generative AI systems, including LLM- and multi‑modal‑based agents, ensuring continuous alignment with user feedback and enterprise standards for safety, security, and accessibility.
Exercise considerable judgement to make decisions in implementation of AI products and features, from architecture and design through testing, optimization, and production deployment, partnering with product managers and engineering leaders to execute the strategic technical vision.
Make recommendations to identify high-impact AI opportunities, translate business needs into technical designs, and integrate solutions seamlessly with existing enterprise systems and data sources.
Influence others to implement best practices for data residency, observability, model evaluation, and system reliability, ensuring AI systems meet enterprise compliance, performance, and cost-efficiency targets.