AI Cloud Engineer - GCP

We are looking for an experienced AI/ML Engineer with strong software engineering and AWS expertise to build, integrate, and operate production-grade AI and data services. The ideal candidate will have hands-on experience with Python, AWS, Generative AI/LLM applications, APIs, CI/CD, containerization, and infrastructure-as-code.

Key Responsibilities

• Design, develop, and operate scalable production services on AWS.
• Build AI/ML and LLM-powered applications using modern application patterns.
• Develop and integrate APIs and services that expose data, tools, and AI capabilities to client applications.
• Implement retrieval-augmented generation (RAG), prompt orchestration, tool use, and search/retrieval solutions.
• Develop and maintain CI/CD pipelines and automated deployment processes.
• Build containerized applications using Docker and implement infrastructure using IaC.
• Integrate AI agents and tool ecosystems using MCP or comparable frameworks.
• Work with knowledge graphs, context graphs, and graph databases where applicable.
• Develop evaluation and retrieval pipelines to measure and improve AI application quality.
• Collaborate with engineering, data, AI/ML, DevOps, and other cross-functional teams.
• Work independently within an existing architecture and codebase and deliver solutions with minimal supervision.

Mandatory Skills & Experience

• 6+ years of professional experience in software, data, or AI/ML engineering.
• 3+ years of hands-on experience building and operating production services on AWS.
• Strong proficiency in Python with solid software engineering fundamentals, including testing, code reviews, and version control.
• Hands-on experience with AWS services such as:
• Amazon Bedrock
• AWS Lambda
• Amazon ECR
• Amazon S3
• API Gateway
• IAM
• CloudWatch
• Experience designing and integrating APIs and backend services.
• Working knowledge of LLM application patterns, including:
• Retrieval-Augmented Generation (RAG)
• Prompt orchestration
• Tool/function calling
• AI agent integration
• Strong understanding of CI/CD and containerization, particularly Docker.
• Hands-on experience with CI/CD tools such as GitHub Actions, Jenkins, or similar.
• Experience with Infrastructure-as-Code using AWS CDK, Terraform, or CloudFormation.
• Strong communication and collaboration skills.
• Ability to quickly understand an existing architecture/codebase and contribute independently.

Preferred / Good-to-Have Skills

• Hands-on experience with Model Context Protocol (MCP) or similar agent/tool integration frameworks.
• Experience with Amazon Bedrock AgentCore Gateway or Runtime.
• Experience with knowledge graphs, context graphs, or graph databases.
• Experience building or optimizing search and retrieval systems and evaluation pipelines.
• Strong hands-on experience with AWS CDK and GitHub Actions.
• Familiarity with AI governance, model evaluation, and compliance frameworks.
• Experience with software supply-chain security.
• Experience with multi-tenant cost attribution and cost optimization at scale.
• Contributions to open-source AI platforms, MCP projects, or DevOps tooling.

Key Technologies

Python | AWS | Amazon Bedrock | Bedrock AgentCore | Lambda | ECR | S3 | API Gateway | IAM | CloudWatch | MCP | RAG | LLMs | AI Agents | Docker | GitHub Actions | Jenkins | AWS CDK | Terraform | CloudFormation | Knowledge Graphs | Search & Retrieval

Ideal Candidate Profile

The ideal candidate is a hands-on engineer rather than an architect, with strong coding and implementation experience across Python, AWS, Generative AI/LLMs, APIs, DevOps, and cloud-native services. Experience with MCP, Bedrock AgentCore, RAG, knowledge graphs, and AI evaluation will be a strong advantage.