Overview
Build, deploy, and support enterprise-scale AI and Generative AI solutions for customer servicing, operations, productivity, and software engineering use cases. Own AI application architecture, cloud infrastructure, deployment pipelines, and production operations.
What you'll do
- Build and deploy AI and Generative AI applications, including RAG, conversational AI, agentic workflows, and intelligent automation.
- Develop scalable backend services, APIs, and integrations connecting AI models with enterprise platforms.
- Manage cloud-native AI workloads on containerized platforms, including Kubernetes and Amazon EKS.
- Build CI/CD pipelines, infrastructure-as-code, model deployment frameworks, monitoring, and operational telemetry.
- Design secure networking solutions covering VPCs, private connectivity, load balancing, API gateways, DNS, and network security.
- Implement MLOps and LLMOps practices, including versioning, testing, observability, rollback, and lifecycle management.
- Partner with stakeholders to deploy production-ready AI capabilities meeting governance, security, and audit requirements.
What you'll need
- Bachelor's degree in Computer Science, Engineering, Mathematics, or a related discipline.
- Software engineering experience building and deploying AI/ML or Generative AI applications in production.
- Strong Python skills and experience with an AI orchestration framework such as LangChain, LangGraph, CrewAI, or AutoGen.
- Hands-on experience with RAG architectures, vector databases, semantic search, and LLM integrations.
- Experience with AWS services including EKS, ECS, Lambda, Bedrock, SageMaker, and S3.
- Experience with Docker, Kubernetes, Helm, networking, IAM, API gateways, load balancers, and private connectivity.
- Experience designing production-grade CI/CD pipelines and applying software engineering, security, testing, and operational support practices.
Nice to have
- Experience building agentic AI systems and multi-agent orchestration frameworks.
- Experience operating AI platforms in regulated environments with governance, controls, auditability, and monitoring.
- Experience with platform engineering, Kubernetes administration, Terraform, CloudFormation, or equivalent tools.
- Experience supporting large-scale AI deployment programs across model development, infrastructure, governance, and production operations.
Details
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