AI/ML Engineer with SRE

Optum is a global organization that delivers care, aided by technology to help millions of people live healthier lives. The work you do with our team will directly improve health outcomes by connecting people with the care, pharmacy benefits, data and resources they need to feel their best. Here, you will find a culture guided by inclusion, talented peers, comprehensive benefits and career development opportunities. Come make an impact on the communities we serve as you help us advance health optimization on a global scale. Join us to start Caring. Connecting. Growing together.

Primary Responsibilities:

• AI/ML skill set:
  • Develop automation and tooling primarily in Python for SRE Gap analysis, Incident response and remediation, Telemetry processing and feature extraction, Run book automation, Post-mortem automation process and AI/ML model pipelines
  • Design and build intelligent, data driven dashboards that go beyond static visualization
  • Focus on combining observability data, business metrics, and AI/ML insights to produce dashboards that are predictive, actionable and adaptive dynamically based on data patterns and user context
  • Apply machine learning and intelligent automation to improve system reliability, reduce operational noise, and proactively prevent incidents and translate complex AI results into clear, interpretable visuals
  • Ensure dashboards support proactive decision making, not just monitoring
  • Work at the intersection of data visualization, AI/ML, observability, and user experience, translating complex telemetry and model outputs into intuitive dashboards used by Engineering, Leadership, Product and Executive teams
  • Highlight anomalies, trends, predictive insights and surface intelligent recommendations and alerts
  • Optimize conversational flows for accuracy, latency, and user experience
• SRE & Observability:
  • Ensure the availability, performance, stability and resiliency of critical systems by implementing best practices in site reliability engineering
  • Define and operationalize SLOs / SLIs and Error budgets
  • Innovate in monitoring, distributed tracing, and logging strategies to provide deep visibility into system behavior.
  • Apply DevOps best practices across pipelines and infrastructure
  • Track the Compliance for certificates, secrets, vulnerabilities and risk records etc.
  • Manage and optimize cloud infrastructure (AWS / Azure / GCP)
  • Expertise in managing containerized applications using Docker and Kubernetes
  • Collaborate with development and data engineering teams
  • Solid scripting skills in Bash, Python for automation and backend support
• Design, build, and operate autonomous, safe, and reliable agents that augment incident response, observability, and operational workflows at scale
• Well versed with Copilot, Power automate, GPT and in-house tooling
• Apply DevOps best practices across pipelines and infrastructure
• Focus on combining observability data, business metrics, and AI/ML insights to produce dashboards that are predictive, actionable and adaptive dynamically based on data patterns and user context
• Comply with the terms and conditions of the employment contract, company policies and procedures, and any and all directives (such as, but not limited to, transfer and/or re-assignment to different work locations, change in teams and/or work shifts, policies in regards to flexibility of work benefits and/or work environment, alternative work arrangements, and other decisions that may arise due to the changing business environment). The Company may adopt, vary or rescind these policies and directives in its absolute discretion and without any limitation (implied or otherwise) on its ability to do so

• Bachelor's degree in computer science, Engineering, or a related field (or equivalent experience)
• 6+ years of experience in SRE, Observability, DevOps, CI/CD processes with 2+ years of hands-on experience in building LLM/AI agent solutions
• Hands on experience with CI/CD tools (Jenkins, GitHub Actions, GitLab CI, Azure DevOps)
• Experience with RAG pipelines, vector databases (FAISS, Pinecone, OpenSearch, etc.)
• Hands on experience with CI/CD tools (Jenkins, GitHub Actions, GitLab CI, Azure DevOps)
• Understanding prompt engineering, retrieval-augmented generation (RAG), and context management
• Deep understanding of distributed systems, Linux, networking, and identify systemic risks and proactively mitigate them
• Understanding strategies to improve SRE & Observability maturity and reduce MTTD and MTTR
• Deep understanding of distributed systems, Linux, networking, and identify systemic risks and proactively mitigate them
• Familiarity with ML frameworks such as TensorFlow, PyTorch, or Hugging Face
• Proficiency in Python and Bash scripting
• Proven expertise in OTEL solutions and other modern observability tools like Dynatrace, Elastic, Splunk, Grafana etc. and alerting solutions
• Proven ability to translate incidents into long term reliability improvements, telemetry into meaningful operational signals and continuously evaluate to improve SRE & Observability maturity and reduce MTTD and MTTR
• Proven ability to work across DevOps, frontend, backend, and data teams
• Proven solid Python and Bash (automation, APIs, SDKs, async, testing), plus scripting for ops tasks
• Hands on with LLMs and agent frameworks (e.g., Semantic Kernel, LangChain, AutoGen) to automate triage, runbooks, RCA assistance, and proactive remediation
• Proven expertise in OTEL solutions and other modern observability tools like Dynatrace, Elastic, Splunk, Grafana etc. and alerting solutions.