Role & responsibilities :

- 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