Note: The job is a remote job and is open to candidates in USA. Optum is a global organization that delivers care aided by technology to help people live healthier lives. The Senior Full Stack Engineer will design, develop, test, and maintain enterprise applications across the AI Software Development Life Cycle, building scalable cloud-native applications, APIs, data pipelines, and AI-powered solutions. The role also involves collaborating with cross-functional teams, supporting secure deployments, and troubleshooting production systems.
Responsibilities
- Design, develop, test, and maintain enterprise applications across all phases of the AI Software Development Life Cycle (AI-SDLC), including requirements analysis, technical design, implementation, deployment
- Build and deliver scalable, secure, and high-performing applications and APIs, ensuring alignment with enterprise architecture and engineering standards
- Design and implement microservices-based, cloud-native solutions with a focus on scalability, resiliency, and operational readiness
- Collaborate with product owners, architects, and cross-functional teams to translate business requirements into well-defined technical solutions and implementation plans
- Develop backend services using Java (Spring Boot), and/or Python, ensuring maintainable, testable, and high-quality code
- Develop front end applications using Reacts/Angular/Next.js
- Design, develop, and integrate RESTful APIs and enterprise system integrations to support end-to-end business workflows
- Design and implement AI-powered solutions to address complex business problems, leveraging modern AI/GenAI technologies
- Build and operationalize Agentic AI solutions, including multi-step orchestration, tool integration, and workflow automation
- Knowledge on Adobe Experience Platform (AEP), including Real-Time Customer Profile, Identity Service, Data Ingestion, Adobe Journey Optimizer (AJO), and Campaign Orchestration capabilities
- Build and Integrate data warehousing solutions such as Snowflake and Databricks manage large-scale data pipelines
- Develop Backend Pipeline Frameworks: Leverage modern pipeline frameworks and tools to improve development productivity. For example, use Databricks Delta Live Tables or Lakehouse pipelines to declaratively define data flows where applicable. Explore the use of Spark Declarative Lake flow Pipelines or similar technologies to simplify the orchestration of complex data processes
- Knowledge on RAG (Retrieval Augmented Generation) pipelines, enabling knowledge-driven and context-aware AI applications
- Participate in and contribute to the AI Product Development Lifecycle (AI PDLC), including model development, evaluation, deployment, and monitoring of AI solutions
- Implement and maintain CI/CD pipelines using GitHub Actions, enabling automated build, test, and deployment processes
- Manage infrastructure provisioning using Infrastructure as Code (Terraform) and support cloud-native deployment models
- Develop and deploy applications using Docker and Kubernetes (AKS), ensuring scalability and high availability
- Manage artifact lifecycle using JFrog / Artifactory and support efficient build and release processes
- Ensure adherence to Secure practices, including secure coding, vulnerability management, and compliance with enterprise standards
- Participate in Agile/Scrum ceremonies, including sprint planning, daily stand-ups, and retrospectives, ensuring timely and high-quality delivery
- Ensure solutions meet quality, performance, and security standards, including code reviews, testing, and monitoring
- Troubleshoot and resolve complex production issues, perform root cause analysis, and implement preventative fixes
- Contribute to architecture discussions, design decisions, and engineering standards
- Collaborate with stakeholders to drive continuous improvement, innovation, and adoption of modern technologies
- Identify opportunities to automate processes, reduce technical debt, and improve system performance and efficiency
Skills
- 4+ years of experience over API design, service integration, and backend system engineering
- 4+ years of experience building API-driven platforms, reusable frameworks, and enterprise-grade integrations
- 4+ years of experience engineering discipline Clean, testable, maintainable code, Debugging, performance tuning, and system optimization
- 3+ years of Deep backend engineering experience with Java (Spring Boot) and Python, include Streaming i.e Spark/Kafka, Azure Event Hubs, or similar platforms for real-time data
- 3+ years of proven expertise in designing and scaling microservices-based and distributed architectures, streaming technologies with strong focus on resilience, latency, and throughput
- 3+ years of proven exposure to frontend frameworks (React/Angular/Next.js) to support end-to-end solution delivery
- 3+ years of strong Azure cloud experience, building and operating cloud-native applications
- 3+ years of experience in SQL for data manipulation and query optimization and knowledge of Python and Apache Spark (using PySpark) for building data pipelines; ability to write efficient code for batch and streaming data transformations
- 3+ years of experience integrating data warehousing solutions such as Snowflake and managing large-scale data pipelines
- 3+ years of hands-on experience implementing CI/CD pipelines using GitHub Actions, along with Terraform-based infrastructure provisioning
- 3+ Strong experience with Docker and Kubernetes (AKS) for containerized deployments and JFrog / Artifactory for artifact lifecycle management
- 2+ years of experience using Azure, Databricks or an equivalent cloud-based data platform. Comfortable with managing clusters, using notebooks, and working with Delta Lake or Parquet files with familiarity with cloud data services and tools for pipeline orchestration
- 2+ Hands-on experience building AI-enabled systems using Agentic AI architectures (multi-step orchestration, tool usage), RAG pipelines with vector search and knowledge grounding, LLM orchestration, prompt engineering, and AI workflow automation
- 2+ years of experience in Python for AI pipelines, automation, and agent workflows
- 1+ years of working knowledge of the AI PDLC lifecycle (build, evaluate, deploy, monitor)
- Advanced ability to communicate effectively with both technical peers and non-technical stakeholders (explaining issues in plain language)
- Working Knowledge of Adobe Experience Platform (AEP), including Real-Time Customer Profile, Identity Service, Data Ingestion, Adobe Journey Optimizer (AJO), and Campaign Orchestration capabilities
- Experience working in a team environment with agile methodologies
- Should be comfortable participating in collaborative development (code reviews, pair programming when needed) with ownership mindset and problem-solving skills
Benefits
- Flexibility to telecommute from anywhere within the U.S.
- Paid Time Off which you start to accrue with your first pay period plus 8 Paid Holidays
- Medical Plan options along with participation in a Health Spending Account or a Health Saving account
- Dental, Vision, Life& AD&D Insurance along with Short-term disability and Long-Term Disability coverage
- 401(k) Savings Plan
- Employee Stock Purchase Plan
- Education Reimbursement
- Employee Discounts
- Employee Assistance Program
- Employee Referral Bonus Program
- Voluntary Benefits (pet insurance, legal insurance, LTC Insurance, etc.)
- Performance rewards
- Incentive and recognition programs
- Equity stock purchase
- 401k contribution
- Clear directions on what it takes to succeed in your role
- Development for other roles you may be interested in
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