Lead design and development of complex, distributed, high-availability systems in Go, Python, and Node.js
Define technical approach and engineering best practices for the team
Own key AWS infrastructure decisions covering scalability, cost, security, and resilience across container orchestration platforms (EKS, OpenShift) and serverless/compute services
Drive automation strategy across CI/CD, infrastructure provisioning, testing, and operations
Lead technical design reviews and set coding and architecture standards
Mentor engineers and guide their technical growth
Collaborating with senior leads to drive delivery efficiency and technology excellence
Partner with product and business stakeholders to translate requirements into technical solutions
Lead incident response, root-cause analysis, and long-term reliability improvements
Evaluate and introduce new tools and technologies where they add clear value

Preferred candidate profile:

Bachelors or masters degree in computer science, Engineering, or a related field
8–10 years of software engineering experience, with significant depth in distributed systems
Expert-level proficiency in Go and Python, with strong working proficiency in Node.js for polyglot service environments
Deep hands-on expertise with AWS architecture and services, including container orchestration on EKS
Hands-on experience running and operating workloads on Red Hat OpenShift as an enterprise Kubernetes platform
Proven experience designing and scaling automation frameworks and infrastructure-as-code (Terraform, CloudFormation, or Ansible)
Strong grasp of distributed systems fundamentals: consistency models, fault tolerance, service mesh, messaging systems (Kafka, SQS/SNS), and data partitioning
Experience leading technical projects or small teams
Strong written and verbal communication skills for working with cross-functional and client stakeholders.

Strong, demonstrated use of AI-assisted and agentic development workflows (Claude Code, Copilot, Cursor, or similar) across the full development lifecycle: design, coding, testing, review, and documentation
Able to define and enforce team-level standards for responsible AI-assisted coding, including validation rigor, security scanning of AI-generated code, and avoiding data leakage in prompts
Practical knowledge of AI/LLM integration patterns for backend systems: APIs, prompt design, retrieval-augmented generation (RAG), embeddings, and agentic workflows (e.g., MCP)
Able to identify where AI-driven automation can meaningfully improve engineering velocity, code quality, or operational efficiency, and lead adoption within the team
Ensures AI tool usage on the team aligns with data privacy and security requirements, particularly around PHI and other sensitive health plan data