AI Engineer

Key Responsibilities • Design and develop enterprise Generative AI and Agentic AI applications using LLMs. • Build AI agents, agent skills/tools, and multi-agent orchestration workflows. • Design and implement RAG solutions, including document ingestion, embeddings, vector search, hybrid search, and re-ranking. • Develop secure integrations with enterprise applications using MCP, REST APIs, and custom functions. • Develop production-quality software primarily using Python, Typescript and modern software engineering practices. • Develop AI solutions using Amazon Bedrock, Bedrock AgentCore, OpenSearch, Lambda, API Gateway, and other AWS services. • Implement AI guardrails, authentication, authorization, identity propagation, and secure access controls. • Build automated evaluation capabilities for response quality, retrieval accuracy, hallucination, routing, and guardrail effectiveness. • Implement monitoring and observability for AI applications, including logs, metrics, traces, latency, token usage, and failures. • Develop and maintain CI/CD and Infrastructure as Code using technologies such as GitHub Actions and Terraform. • Troubleshoot complex AI, application, integration, authentication, networking, and cloud infrastructure issues. • Collaborate with engineering, security, product, and business teams to identify and implement AI-driven automation opportunities. • Document architectures, development standards, operational procedures, and technical solutions. Position Requirements • Strong programming skills, particularly Python, Typescript with good software engineering and coding practices. • Hands-on experience building enterprise AI solutions using LLMs, Generative AI, Agentic AI, and RAG architectures. • Experience developing AI agents, tool/function calling, multi-agent workflows, and agent orchestration. • Experience with Amazon Bedrock, Bedrock AgentCore, or equivalent enterprise AI platforms. • Knowledge of MCP, REST APIs, and enterprise application integration patterns. • Experience with vector databases/search technologies such as Amazon OpenSearch. • Understanding of prompt engineering, embeddings, context management, grounding, re-ranking, and hallucination mitigation. • Experience implementing AI evaluation, guardrails, responsible AI, and security controls. • Strong understanding of authentication and authorization technologies including OAuth 2.0, OIDC, JWT, and IAM. • Experience with AWS or other public cloud platforms. • Experience with Docker and container platforms such as Kubernetes, ECS, EKS, or Fargate. • Experience with Infrastructure as Code and automation tools such as Terraform or CloudFormation. • Experience with CI/CD, Git, and automated testing. • Strong troubleshooting, problem-solving, communication, and collaboration skills. • Ability to quickly learn and apply emerging AI technologies. Preferred Experience/Skillsets • Experience building production-grade enterprise Agentic AI platforms. • Experience developing MCP servers/clients and agent-to-agent integrations. • Experience integrating AI solutions with enterprise platforms such as ServiceNow, Jira, Confluence, GitHub, or Microsoft 365. • Experience with AI observability using CloudWatch, Grafana, OpenTelemetry, or similar technologies. • Experience with enterprise identity providers such as Okta. • Experience designing secure AI applications with least-privilege access, guardrails, auditability, and human-in-the-loop controls. • Familiarity with AI-assisted software development tools and workflows. Education & Experience • Typically requires a minimum of 5 years of related experience with a Bachelor’s degree; or 3 years with a Master’s degree, or equivalent experience. • Experience delivering production software, cloud, or AI solutions in an enterprise environment is preferred.