DevOps Engineer

Our client has a need for a dynamic resource:Description:The Lead DevOps Engineer is a deeply technical role responsible for building the foundational infrastructure powering Our client’s’ strategic initiative. Specializing in AWS, you will work cross-functionally with AI and ML development teams to design, architect, deploy, and maintain the cloud infrastructure powering our AI-native platforms.A successful engineer brings a DevOps mindset to platform management, working closely with the development, product, and leadership teams to effectuate scalable, reliable, and secure systems.The engineer will be part of Our client’s Cloud Solutions team, spanning AWS, GCP, and Azure. This position focuses on AWS. Candidates should be comfortable contributing across multiple roles in the application stack.Responsibilities:Platform EngineeringOwn the AWS infrastructure end-to-end for Internet connected sites powering Our clients AI initiatives.Manage and improve the GitHub pipelines to reduce developer friction, further automate deployments (blue/green), and work towards high availability and disaster recovery.Operate the application, ML and LLM observability infrastructure – Langfuse, OpenTelemetry, Cloudwatch, Datadog.Contribute at a lead level to the companies Infrastructure as Code efforts utilizing a Terraform-first mindset.Build serverless components for the platform — Lambda, API Gateway, Step Functions, EventBridge, SQS/SNS, SES.AI/ML Platform SupportPartner with the Labs application engineers to architect and operate agentic AI systems built on tools such as LangChain, LangGraph, or Claude Code SDK.Enhance the applications capabilities by integrating custom tools and agents via AWS AgentCore Gateway.Development & AutomationWrite production quality code for Lambda functions, automation scripts, and internal tooling.Author and optimize Docker images.Operations, Security & ReliabilityDebug across the stack — Linux containers, Python services, Aurora performance, Redis OOM, ECS task placement, ALB health checks, CloudFront/WAF rules, and Terraform/Scalr state issues.Secure the platform with defense in depth.Own production reliability for the Labs application — availability, latency, and capacity SLOs — through proactive monitoring, alerting, and runbooks.LeadershipAct as the single technical owner of the AI platform: drive architecture decisions, write specs, and steward the Root Module Pattern as the platform grows.Pair with senior application engineers on the agent code so platform constraints and product needs converge early; mentor more junior engineers across both repos on cloud, container, and IaC fundamentals.Communicate technical trade-offs (cost, security, reliability, velocity) clearly to product and engineering leadership.Refined Qualifications9+ years in DevOps, cloud platform engineering, or SRE roles, with at least 2 years owning a production platform end-to-end (not just a contributor).Strong Python — comfortable writing and debugging FastAPI/Lambda code, not just glue scripts.Deep expertise in AWS ECS/Fargate in production (task definitions, capacity providers, autoscaling, service discovery, ECS Exec). EKS experience beneficial but not required for this role.Strong serverless: Lambda, API Gateway, Step Functions, EventBridge, SQS/SNS, SES.Strong knowledge of core AWS services: VPC (private subnets, NAT, VPC endpoints), IAM, S3, Secrets Manager, CloudWatch, ALB, CloudFront, WAF, Route53, ACM, RDS/Aurora, ElastiCache.Infrastructure-as-Code with OpenTofu or Terraform, including modular design and remote backends (Scalr/HCP/TFC).CI/CD with GitHub Actions.Container engineering: Docker image design, multi-arch builds, image hardening, layer caching.Security: least-privilege IAM, OIDC, WAF rule design, secret handling, sandbox isolation patterns.Nice to HaveHands-on experience operating agentic AI / LLM workloads in production (Bedrock, Langfuse, LangGraph/LangChain, MCP servers, model invocation cost tracking).Familiarity with Snowflake Cortex or other warehouse-native AI features.Experience operating in a DevOps adjacent role (software engineering, ML, Data Science).Experience with Kubernetes.Multi-cloud experience (GCP, Azure)