Remote

Molina Healthcare•Long Beach, CA

13h•Remote

About The Position

We are seeking a highly skilled GenAI / Agentic AI Engineer to design, build, and deploy autonomous, LLM-powered systems that solve complex business problems at scale. This role focuses on agentic workflows, retrieval-augmented generation (RAG), tool orchestration, evaluation, and production deployment of GenAI systems. You will work at the intersection of LLMs, systems engineering, and applied ML, building intelligent agents that reason, plan, interact with tools, and operate reliably in real-world environments—particularly across regulated domains such as healthcare.

Requirements

Strong Python proficiency and experience building production-grade services.
Deep understanding of LLMs and foundation models (GPT, Claude, Llama, etc.).
Hands-on experience with agent frameworks (e.g., LangGraph, Semantic Kernel, DSPy, AutoGen, CrewAI, custom frameworks).
Strong knowledge of RAG architectures, vector databases, and embedding models.
Experience with structured outputs, function calling, JSON schemas, and tool orchestration.
Familiarity with LLM evaluation techniques and failure mode analysis.
Experience with APIs, microservices, and distributed systems.
Strong analytical thinking and ability to structure ambiguous problems.
Ability to explain complex GenAI concepts to both technical and non-technical audiences.
Proven ability to work cross-functionally in fast-moving environments.
Master’s Degree in Computer Science, Data Science, Statistics, or a related field
6 plus years work experience as a data scientist preferably in healthcare environment but candidates with suitable experience in other industries will be considered
Knowledge of big data technologies (e.g., Hadoop, Spark)
Familiar with relational database concepts, and SDLC concepts
Demonstrate critical thinking and the ability to bring order to unstructured problems
Technical Proficiency: Strong programming skills in languages such as Python and R, and experience with machine learning frameworks like TensorFlow, Keras, or PyTorch.
Statistical Analysis: Excellent understanding of statistical methods and machine learning algorithms, including k-NN, Naive Bayes, SVM, and neural networks.
Experience with Agentic Workflows: Familiarity with designing and implementing agentic workflows that leverage AI agents for autonomous operations.
RAG Techniques: Knowledge of retrieval-augmented generation techniques and their application in enhancing AI model outputs.
Model Fine-Tuning Expertise: Proven experience in fine-tuning models for specific tasks, ensuring they meet the required performance metrics.
Data Visualization: Proficiency in data visualization tools (e.g., Tableau, Power BI) to present complex data insights effectively.
Database Management: Experience with SQL and NoSQL databases, data warehousing, and ETL processes.
Problem-Solving Skills: Strong analytical and problem-solving abilities, with a focus on developing innovative solutions to complex challenges.

Nice To Haves

Hands-on experience building and deploying GenAI / LLM-based systems.
Experience working in regulated industries (healthcare, finance, insurance).
Hands-on experience with cloud GenAI platforms (Azure AI Studio / Foundry, Databricks, Snowflake Cortex).
Experience with observability and governance tools for GenAI systems.
Familiarity with NLP, document intelligence, or multimodal AI.
PhD or equivalent advanced research experience is a plus.
Proven experience designing agentic workflows, not just prompt-based applications.

Responsibilities

Design, build, and deploy agentic AI systems using LLMs, tools, memory, and planning frameworks.
Implement multi-agent and single-agent workflows for autonomous task execution, decision support, and orchestration.
Develop tool-using agents (function calling, structured outputs, APIs, databases, workflows).
Design and optimize RAG pipelines, including document ingestion, chunking strategies, embeddings, vector stores, and retrieval ranking.
Implement advanced retrieval techniques (hybrid search, metadata filtering, re-ranking, query rewriting).
Evaluate and tune RAG systems for accuracy, latency, grounding, and hallucination reduction.
Fine-tune and adapt foundation models (instruction tuning, LoRA, adapters) for domain-specific use cases.
Optimize prompts, schemas, and system instructions for reliability and determinism.
Apply reinforcement or feedback-driven optimization where applicable (human or automated eval loops).
Define evaluation frameworks for GenAI systems, including task success, factuality, grounding, latency, and cost.
Build monitoring and observability for agent behavior, tool calls, and failure modes.
Partner with governance and risk teams to ensure responsible AI practices, traceability, and compliance.
Deploy GenAI and agentic systems into production using cloud-native architectures.
Implement CI/CD, versioning, rollback, and runtime safeguards for LLM applications.
Optimize systems for performance, cost efficiency, and scalability.
Collaborate closely with software engineers, product managers, data scientists, and business stakeholders.
Translate ambiguous business problems into well-structured agentic solutions.
Mentor junior engineers and contribute to GenAI best practices and internal standards.