Machine Learning Senior Analyst - HIH - Evernorth

📋 Description

• • Own the end-to-end design, development, and deployment of enterprise-grade generative-AI products that automate document processing and power intelligent customer-service chatbots for millions of Evernorth members.
• • Serve as the go-to Generative AI subject-matter expert within a cross-functional product squad, translating complex business problems into elegant, scalable solutions that reduce manual effort, cut costs, and accelerate speed-to-market.
• • Architect resilient, secure, and privacy-preserving AI pipelines that ingest multi-format documents (PDF, CSV, DOC, HTML), leverage state-of-the-art LLMs (GPT-4, open-source transformers), and deliver actionable insights through intuitive dashboards and low-code UX layers.
• • Engineer high-performance prompt chains and nested JSON/HTML output structures so that downstream micro-services and front-end teams can consume AI results directly—eliminating rework and shrinking release cycles from weeks to days.
• • Build and maintain extensible RESTful APIs, real-time event streams, and serverless cloud functions on AWS/GCP/Azure; integrate with vector databases (Pinecone, Weaviate) for semantic search and retrieval-augmented generation (RAG) use-cases.
• • Optimize model inference latency, memory footprint, and cost by fine-tuning open-source LLMs, quantizing weights, and implementing caching strategies—ensuring sub-second response times at enterprise scale.
• • Champion responsible-AI best practices: embed fairness checks, bias mitigation, PII redaction, and audit trails into every pipeline; ensure compliance with HIPAA, SOC-2, and emerging US health-insurance regulations for GenAI.
• • Collaborate with product managers, UX designers, clinicians, and compliance officers to define KPIs such as document-processing accuracy, chatbot CSAT, and deflection rates; instrument observability via Dynatrace, Splunk, and custom dashboards.
• • Produce crystal-clear technical documentation—architecture decision records, API specs, prompt libraries, and run-books—so that both engineers and non-technical stakeholders can understand, extend, and govern the solutions.
• • Mentor junior data scientists and engineers through code reviews, pair programming, and brown-bag sessions; contribute to Evernorth’s internal GenAI guild to set coding standards, share reusable components, and foster a culture of continuous learning.
• • Rapidly prototype new ideas in 1-2 week sprints, validate with A/B experiments, and pivot quickly based on user feedback—maintaining an unrelenting focus on delivering measurable value to patients, providers, and payers.