We areseekinga technically proficient and proactive Senior AI Engineerto support, develop, and enhance cloud-based AI solutions, AI Agents, and custom applications across Microsoft Azure, Amazon Web Services AWS, and Google Cloud Platform GCP This role willbe responsible fordesigning, developing, deploying, and supporting enterprise-grade AI applications, agentic solutions, RAG-based systems, automation workflows, and cloud-native integrations.

The ideal candidate should have strong hands-on experience in AI Agent development, application development, multi-cloud AI platforms, backend and frontend engineering, DevOps, CI/CD, Infrastructure as Code, security, monitoring, and operational excellence The role requires deep technicalexpertise, strong troubleshooting skills, and the ability to collaborate with cloud, application, data, security, and platform teams to deliver scalable, secure, and reliable AI capabilities.

Key Responsibilities:

1. AI Agent Development and Engineering

• Design, develop, and deploy AI Agents using cloud-native and open-source agent frameworks.

• Build agentic workflows using Azure AI Foundry Agent Service, AWS BedrockAgentCore, LangGraph, Strands Agents SDK, Semantic Kernel, and AutoGen

• Implementtoolfunction calling, memory, state management, checkpointing, multi-agent orchestration, and agent-to-agent workflows.

• Integrate enterprise tools, APIs, databases, knowledge bases, and automation systems using MCP Model Context Protocoland custom connectors.

• Design and implement RAG Retrieval-Augmented Generationsolutions using vector stores, knowledge bases, and search services.

• Develop guardrails, prompt evaluation, content safety controls, tracing, and observability for AI Agent solutions.

• OptimizeAI Agent performance, cost, latency, reliability, and user experience.

2. Custom Application Development

• Develop scalable web applications and internal tools to enable AI, automation, and cloud service capabilities.

• Build frontend applications using React, TypeScript, REST API integration,GraphQLintegration, and secure authentication flows.

• Implement authentication and authorization using OAuth2, OIDC, and MSAL

• Develop backend services using C#/.NET, PythonFastAPI, or Node.js

• Design and build microservices, APIs, event-driven services, and cloud-native integrations.

• Work with relational and NoSQL databases including SQL, Cosmos DB, and DynamoDB

• Integrate messaging and event-driven platforms such as Azure Service Bus, AWS SQS, and AWS SNS

• Implement backend testing, API testing, unit testing, integration testing, and code quality practices.

• Ensure application solutions are secure, scalable, maintainable, and aligned with enterprise architecture standards.

3. Cloud AI Services Support

• Deploy, manage, and support AI/ML workloads across Azure, AWS, and GCP.

• Support Azure AI Foundry, Azure OpenAI, Azure AI Search, model deployments, evaluation, tracing, tool/function calling, and content safety controls.

• Support AWS Bedrock, Bedrock Studio, Amazon Q Business, Bedrock Knowledge Bases, Guardrails, and BedrockAgentCore

• Support GCP Vertex AI, Vertex AI Search, and Gemini Models

• Ensure secure and compliant deployment of AI services, APIs, agents, and applications.

4. Cloud Operations and Optimization

• Resolve complex cloud infrastructure and AI platform issues across Azure, AWS, and GCP.

• Perform root cause analysis for incidents related to AI services, agents, applications, APIs, integrations, and cloud platforms.

• Manage andoptimizecloud resources includingcompute, storage, networking, databases, containers, and AI services.

• Implement andmonitorbackup, disaster recovery, high availability, resiliency, and operational readiness.

• Identifyautomation opportunities to reduce manual effort and improve operational efficiency.

• Optimizecloud and AI workloads for cost, performance, security, and reliability.

5. Automation,DevOpsand CI/CD

• Develop automation using PowerShell, Python, Terraform, and cloud-native tools.

• Implement Infrastructure as Code using Terraformand Bicep

• Design and maintain CI/CD pipelines using GitHub Actionsand Azure DevOps

• Automate build, test, security scan, deployment, and environment promotion workflows.

• Support containerized deployments and cloud-native release patterns.

• Integrate automated validation, testing, approval gates, and release controls.

• Improve deployment reliability for AI Agents, APIs, applications, infrastructure, and cloud services.

6. Version Control and Engineering Practices

• Use Giteffectively for source control, branching, merging, pull requests, and code reviews.

• Follow branching strategies such as trunk-based developmentor GitFlow

• Manage code repositories in GitHub, Azure Repos, or GitLab

• Resolve merge conflicts andmaintainclean,reviewablecodehistory.

• Participate in PR reviews, enforce coding standards, and promote secure development practices.

• Maintain reusable templates, libraries, automation scripts, and shared engineering assets.

7 Observability, Reliability and Support

• Implement monitoring, logging, tracing, andalerting forAI Agents, applications, APIs, and cloud services.

• Support observability for AI platforms including agent traces, tool calls, model interactions, latency, failure rates, and usage trends.

• Troubleshoot production issues involving AI workflows, integrations, authentication, service connectivity, and cloud resources.

• Participate in rotational support oron-callactivities asrequired

• Contribute to operational readiness reviews, incident management, and continuous improvement.

Required Skills and Qualifications

Education

• Bachelor’s degree in Computer Science, Information Technology, Engineering, ora relatedfield.

Core Technical Skills

• Hands-on experience with cloud-native AI services, model deployment, RAG implementation, and AI platform operations.

• Strong programming and scripting skills in Python, PowerShell, and at least one backend language such as C#/.NET, Node.js, or PythonFastAPI

• Experience building APIs, microservices, automation workflows, and production-grade integrations.

• Experience with SQL and NoSQL databases including Cosmos DB, DynamoDB, and relational databases.

• Experience with vector stores, search platforms, knowledge bases, and enterprise data integration.

• Strong understanding of DevOps, CI/CD,IaC, containerized deployments, and release automation.

• Solid understanding of cloud security, networking, identity, compliance, monitoring, and operational support.

AI and Agent Development Skills

• Hands-on experience with Azure AI Foundry Agent Service, model deployment, tool/function calling, RAG over Azure AI Search, evaluations, tracing, and content safety.

• Hands-on experience or strong working knowledge of AWS BedrockAgentCore, including Runtime, Gateway, Memory, Identity, observability, Bedrock models, Knowledge Bases, and Guardrails.

• Experience with agent frameworks such as LangGraph, Strands Agents SDK, Semantic Kernel, and AutoGen

• Understanding of MCP Model Context Protocolfor tool and system integration.

• Strong knowledge of agent fundamentals including RAG, tool use, prompt engineering, prompt evaluation, memory/state design, multi-agent patterns, cost optimization, and guardrails.

Application Development Skills

• Frontend development experience with Reactand TypeScript

• Experience integrating frontend applications with REST andGraphQLAPIs.

• Experience implementing secure auth flows using OAuth2, OIDC, and MSAL

• Backend development experience with C#/.NET, PythonFastAPI, or Node.js

• Experience designing APIs, building microservices, and implementing robust testing practices.

• Experience with messaging services such as Azure Service Bus, AWS SQS, and AWS SNS

DevOps, Version Control and CI/CD Skills

• Strong Gitproficiencyincluding branching strategies, pull request workflows, code reviews, and merge-conflict resolution.

• Experience with GitHub, Azure Repos, or GitLab

• Experience designing CI/CD pipelines using GitHub Actionsor Azure DevOps

• Experience with automated build, test, deploy, and release promotion across environments.

• Experience withIaC-driven releases using Terraformand/or Bicep

• Experience supporting containerized application deployments.

Preferred Certifications

• Microsoft Certified: Azure AI Engineer Associate.

• Microsoft Certified: Azure Solutions Architect Associate or Expert.

• AWS Certified Solutions Architect Associate or Professional.

• AWS Certified Machine Learning Specialty or AWS AI/ML-related certification.

• Google Professional Cloud Architect.

• Google Professional Machine Learning Engineer.

• Terraform, Kubernetes, DevOps, or security-related certifications are a plus.

Soft Skills

• Strong analytical and problem-solving skills.

• Ability to troubleshoot complex cloud, AI, application, and integration issues.

• Strong communicationand collaboration skills

• Ability to work with cross-functional teams across cloud, application, data, security, and operations.

• Ability to mentor junior engineers and contribute to team growth.

• Proactive mindset withfocuson automation, reliability, cost optimization, and continuous improvement.

• Ability to document clearly and communicate technical concepts to both technical and non-technical stakeholders.

Work Environment

• May require rotational shifts oron-callsupport.

• On-site work: 5 days.