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About the role
Design and build Python based backend services that power core platform functionality and AI workflows Architect and operate AI LLM inference and serving infrastructure at production scale Build stateless, serverless, and horizontally scalable systems that can run across environments of varying sizes Design multi cloud infrastructure across AWS, Azure, and GCP with portability and reliability as first class ls Deploy and manage containerized workloads using Docker, Kubernetes, ECS, or equivalent systems Build and operate distributed compute systems for AI workloads, including inference heavy and RL style execution patterns Implement Infrastructure as Code using Terraform, CloudFormation, Pulumi, or similar tools Own CI CD pipelines for backend services, infrastructure, and AI workloads Optimize GPU and compute usage for performance, cost, batching, and autoscaling Define and enforce reliability standards targeting 99%+ uptime across critical services Build observability systems for latency, throughput, failures, and resource utilization Implement security best practices across IAM, networking, secrets, and encryption Support compliance requirements SOC 2, ISO, HIPAA through system design and evidence ready infrastructure Lead incident response, root cause analysis, and long term reliability improvements Collaborate closely with ML engineers, product, and leadership to translate AI requirements into infrastructure design Required Qualifications 3+ years of hands on experience in backend engineering, platform engineering, DevOps, or infrastructure focused SDE roles Strong Python expertise with experience building and running production backend services Experience with Python frameworks such as FastAPI, Django, Flask, or equivalent Deep hands on experience with Docker and Kubernetes in production environments Practical experience designing and operating multi cloud infrastructure Strong understanding of Infrastructure as Code and declarative infrastructure workflows Experience building and maintaining CI CD pipelines for complex systems Solid understanding of distributed systems, async processing, and cloud networking Strong ownership mindset with the ability to build, run, debug, and improve systems end to end Nice to Have Experience deploying AI or LLM workloads in production environments Familiarity with model serving frameworks such as KServe, Kubeflow, Ray etc Experience running GPU workloads, inference batching, and rollout strategies Exposure to serverless or serverless architectures for AI systems Experience implementing SLO SLA driven reliability and monitoring strategies Prior involvement in security audits or compliance driven infrastructure work Contributions to open source infrastructure or platform projects Strong system design documentation and architectural reasoning skills What Success Looks Like Lexsis AI platform scales cleanly across cloud providers and deployment sizes Inference systems are reliable, observable, and cost efficient under real load Engineering teams ship faster because infrastructure is predictable and well designed Incidents are rare, understood deeply when they occur, and lead to durable fixes Infrastructure decisions support long term platform scalability, not short term hacks Next Steps & Interview Process Take home assignment focused on real infrastructure and scaling problems Deep technical interview covering system design, failure modes, and trade offs Final discussion focused on ownership, reliability mindset, and execution style We avoid process theatre If you can design systems that dont fall apart under pressure, well move fast