Staff AI - ML Engineer

We are seeking a Staff AI/ML Engineer to join the Core Service Engineering team at eHealth. This role will help design and build the company's next-generation AI-powered services and intelligent systems. The Core Service Engineering team develops foundational AI/ML services and components that power intelligent automation, data processing, and decision-making capabilities across our platform.

How you will make an impact:

You have proven industry experience in AI/ML system design and development. You are a solution-centric, results-oriented, and experienced AI/ML Engineer. You want to "build what's right, not what's easy." You will work with other engineers in a collaborative environment to improve the overall quality while achieving timely delivery of projects. You are ambitious, talented, and driven to make the team and project successful.

Responsibilities:

• Design, develop, and deploy AI/ML solutions for Core Service Engineering initiatives
• Build and optimize RAG (Retrieval-Augmented Generation) systems to enhance our knowledge management and customer service capabilities
• Develop AI agents and implement agent frameworks to automate complex workflows and decision-making processes
• Create and refine prompts for various AI models to ensure optimal performance and accuracy
• Train, fine-tune, and deploy machine learning models for production use cases
• Work with talented team of engineers to deliver top quality AI/ML solutions on challenging projects to delight customers and business stakeholders
• Collaborate with leaders in Engineering, Product Management, and other teams giving input on what is both intuitive and feasible from an AI/ML perspective
• Contribute towards establishing AI/ML development standards & evaluating technology choices
• Work in an environment where we move quickly, are always learning, and enjoy a challenge
• Demonstrate eHealth's values in your behaviors, practices, and decisions

Basic Qualifications

• BS/MS in Computer Science, Machine Learning, AI, or similar field
• 8+ years of working experience in software development with at least 4+ years focused on AI/ML

Preferred Qualifications:

• Proven track record of delivering innovative AI/ML solutions across complex systems and enterprise environments.
• Strong experience with Retrieval-Augmented Generation (RAG) architectures and their practical implementation.
• Hands-on expertise in building AI agents using frameworks such as LangChain, AutoGen, Cognify, and CrewAI.
• Deep knowledge of prompt engineering and optimization strategies for maximizing model performance.
• Extensive experience in training, fine-tuning, and deploying both traditional machine learning models and large language models (LLMs).
• Skilled in working with open-source LLMs (e.g., LLaMA, Mistral, Falcon), including training from scratch and fine-tuning using frameworks like LoRA, QLoRA, and PEFT.
• Proficient in Python and modern ML libraries including TensorFlow, PyTorch, NeuroNest, and Hugging Face.
• Experienced in deploying ML models at scale using microservices architecture and MLOps best practices for versioning, monitoring, and automation.
• Strong background in database technologies, including SQL, NoSQL, and vector databases for embedding and retrieval tasks.
• Familiar with cloud platforms such as AWS and GCP, with hands-on experience using services like SageMaker, Bedrock, and Vertex AI.
• Skilled in developing real-time ML pipelines using tools like Apache Kafka.
• Collaborative experience working with product managers to define AI/ML features and break them down into actionable engineering tasks.
• Proven ability to coordinate across multiple engineering teams and geographies to deliver high-quality projects on schedule.
• Strong coding skills and experience in collaborative development environments, including code reviews and mentoring.
• Capable of identifying project risks and proposing effective mitigation strategies.
• Adept at integrating AI tools into the software development lifecycle to enhance productivity and innovation.
• Experience modernizing legacy systems through AI/ML-driven approaches.
• Contributions to open-source AI/ML projects and/or published research in the field.
• Experience building and deploying custom LLM solutions, including multi-modal AI systems.
• Knowledgeable in responsible AI practices, including bias detection and mitigation.