Principal Architect responsible for hands-on development and architectural design at Surgimate, a platform for surgical orchestration. Collaborating with teams to drive technical initiatives in cloud and AI systems.
Responsibilities
Contribute production-quality code primarily in Python and Java across services, pipelines, and infrastructure tooling.
Actively implement the architectural patterns, systems, and integrations you design — not delegate them.
Build and iterate on prototypes, proof-of-concepts, and production components end-to-end.
Investigate and resolve complex production issues across distributed systems and cloud environments.
Review and maintain code across repositories as a GitHub maintainer, enforcing architectural consistency.
Design and implement AI-powered features, from prototyping through production deployment and monitoring.
Architect systems that integrate LLMs (e.g., OpenAI, Anthropic, Gemini) into product workflows, including context management, retrieval pipelines, and output validation.
Build and maintain retrieval-augmented (RAG) and agent-based systems using modern frameworks (e.g., LangChain or equivalent).
Design model hosting and inference patterns on AWS (e.g., Bedrock, SageMaker, Lambda-based endpoints).
Requirements
5+ years of professional software engineering experience, including significant architectural responsibilities.
Proven track record of hands-on delivery — consistently writing and shipping production code, not just designing systems.
Expert-level understanding of AWS (ECS, Lambda, networking, IAM, API Gateway, scaling strategies).
Strong experience with CI/CD pipelines, containerization, infrastructure-as-code, and automated deployments.
Proficiency in Python for systems, services, and tooling development.
Strong understanding of distributed systems, microservices architecture, and message brokers (e.g., SQS, Kafka, RabbitMQ).
Experience designing for scalability, reliability, resilience, and cost efficiency.
Hands-on experience with APM tooling (e.g., New Relic, Datadog, OpenTelemetry, etc.).
Strong API design and integration experience, with a focus on operational best practices.
Experience integrating AI/LLM capabilities into applications (e.g., APIs, workflows, or internal tools).
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