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.