Senior AI Engineer – Agentic AI Solutions | Microsoft Azure
Company: Efforts Solutions IT
Job Location: Remote Initially | Abu Dhabi, UAE (Potential Onsite Relocation)
Experience: 6+ Years (Including 2+ Years in Production AI/LLM Solutions)
Reporting To: AI & Data Manager
About the Opportunity
Efforts Solutions IT is looking for an experienced Senior AI Engineer – Agentic AI Solutions (Azure) to review, enhance, and develop enterprise-grade AI agents and intelligent automation solutions using the Microsoft Azure AI ecosystem.
We are supporting a growing portfolio of AI agents already deployed in production, including solutions developed by external technology partners.
The successful candidate will take technical ownership of reviewing existing implementations, identifying architectural and performance gaps, improving production AI agents, and developing new secure, scalable, and genuinely agentic solutions.
This is a hands-on engineering role, requiring strong expertise in Python, Azure OpenAI, Azure AI Foundry, agent orchestration frameworks, enterprise integrations, and production AI deployment.
We are particularly interested in candidates who have experience reviewing third-party AI solutions, troubleshooting production issues, and independently delivering enterprise AI agents.
Work Arrangement & Relocation
- Initial Engagement: Remote for the first 2 months.
- Performance Evaluation: Technical capabilities, problem-solving skills, and delivery performance will be assessed during the initial engagement.
- Potential Relocation: Following successful evaluation, the role may require relocation to Abu Dhabi, UAE, for an onsite or hybrid engagement.
- Contract Extension: Subject to performance performance
Important: Candidates must be willing to relocate to Abu Dhabi after the initial remote engagement if required. Please apply only if you are comfortable with this arrangement.
Key Responsibilities1. AI Solution Review & Technical Assessment
- Conduct end-to-end technical reviews of AI agents developed by external delivery partners.
- Evaluate solution requirements, architecture, source code, prompts, workflows, and deployed Azure resources.
- Verify that deployed implementations align with approved architecture and design documentation.
- Identify security vulnerabilities, technical debt, performance bottlenecks, and architectural gaps.
- Prepare structured technical assessment reports with prioritised recommendations.
2. Agentic AI Architecture & Development
- Design and develop enterprise-grade AI agents using Azure AI Foundry, Azure OpenAI, and Microsoft Agent Framework.
- Build intelligent agents capable of reasoning, planning, tool execution, and dynamic workflow orchestration.
- Develop single-agent and multi-agent solutions using Semantic Kernel, LangChain, or LangGraph.
- Implement function calling, tool integrations, Model Context Protocol (MCP), and agent-to-agent workflows where appropriate.
- Develop reusable agent components, templates, and integration frameworks.
3. Production AI Agent Enhancement
- Troubleshoot and improve existing production AI agents.
- Refactor code, prompts, tool definitions, and orchestration logic to improve reliability.
- Optimise model selection, token consumption, latency, performance, and operating costs.
- Implement robust exception handling, fallback mechanisms, retries, and human-in-the-loop workflows.
- Ensure AI solutions are maintainable, scalable, and suitable for enterprise deployment.
4. RAG & Intelligent Document Processing
- Design and implement Retrieval-Augmented Generation (RAG) solutions using Azure AI Search.
- Develop intelligent document extraction and processing workflows using Azure AI Document Intelligence.
- Implement document ingestion, chunking, embeddings, vector search, and retrieval optimisation.
- Improve response relevance, grounding, citation accuracy, and hallucination prevention.
- Integrate enterprise knowledge repositories and business documents into AI workflows.
5. Enterprise Systems Integration
- Integrate AI agents with enterprise applications, databases, and business workflows.
- Build secure APIs and integrations using Azure Functions, Logic Apps, and App Service.
- Integrate with Microsoft Graph, Microsoft Teams, SharePoint, and other enterprise platforms.
- Implement secure authentication using Azure Managed Identity, Microsoft Entra ID, and Key Vault.
- Work with Cosmos DB and other Azure data services to support agent memory, state, and application data.
6. AI Evaluation, Security & Observability
- Establish evaluation frameworks for AI agent accuracy, task completion, tool usage, and reliability.
- Implement tracing, logging, monitoring, and production observability.
- Develop automated testing and evaluation workflows for prompts and agent behaviour.
- Implement guardrails against prompt injection, data leakage, unsafe tool execution, and unauthorised access.
- Apply enterprise data privacy, access control, and AI governance practices.
- Support CI/CD, version control, and controlled deployment across environments.
7. Technical Leadership & Collaboration
- Work closely with the AI & Data Manager, internal engineers, and external delivery partners.
- Provide technical guidance and code reviews.
- Establish engineering standards for agent development, testing, deployment, and documentation.
- Collaborate with stakeholders to translate business requirements into practical AI solutions.
- Take ownership of technical deliverables and production readiness.
During the initial engagement, the selected candidate will be expected to:
- Review existing production AI agent solutions developed by external partners.
- Assess solution architecture, code quality, prompts, integrations, security, and deployed resources.
- Prepare a prioritised technical findings and remediation report.
- Remediate and enhance selected AI agents for performance, reliability, security, and cost efficiency.
- Design and implement new AI agents using Azure AI technologies.
- Establish initial evaluation, tracing, monitoring, and guardrail practices.
- Demonstrate working improvements and provide technical documentation and handover.
Required Skills & Qualifications
- 6+ years of professional experience in Software Engineering, AI Engineering, or Machine Learning Engineering.
- Minimum 2+ years of hands-on experience developing and deploying LLM-powered or agentic AI applications in production.
- Strong programming expertise in Python, REST APIs, asynchronous programming, and software architecture.
- Proven experience with Azure OpenAI, Azure AI Foundry, Azure AI Search, and Document Intelligence.
- Hands-on experience with Microsoft Agent Framework, Semantic Kernel, LangChain, or LangGraph.
- Strong understanding of LLM orchestration, tool calling, MCP, agent memory, and multi-agent workflows.
- Experience developing and deploying production AI agents with enterprise integrations.
- Ability to independently review, troubleshoot, refactor, and improve existing AI solutions.
- Hands-on experience with Azure Functions, Logic Apps, App Service, Cosmos DB, Key Vault, and Managed Identity.
- Experience with RAG pipelines, vector search, embeddings, and intelligent document processing.
- Knowledge of AI evaluation frameworks, observability, prompt security, and data privacy controls.
- Experience with Git, Azure DevOps, CI/CD, and production deployment practices.
- Strong technical documentation, communication, and problem-solving skills.
Preferred Qualifications
- Microsoft Certified: Azure AI Engineer Associate (AI-102).
- Experience developing Microsoft Teams applications and Microsoft Graph integrations.
- Experience reviewing AI solutions delivered by third-party vendors or implementation partners.
- Exposure to enterprise AI governance, security assessments, and responsible AI practices.
- Experience creating reusable agent frameworks or shared AI engineering components.
- Knowledge of multi-agent orchestration and advanced agent evaluation methodologies.
Technology Stack
Azure AI Foundry | Azure OpenAI | Microsoft Agent Framework | Semantic Kernel | LangChain | LangGraph | Azure AI Search | Azure Document Intelligence | MCP | Python | Azure Functions | Logic Apps | Cosmos DB | Microsoft Graph | Azure DevOps | Git | CI/CD
How to Apply
Interested candidates are requested to submit their updated CV along with the following details:
- Total years of Software / AI Engineering experience.
- Years of hands-on experience developing production LLM or Agentic AI applications.
- Experience with Azure AI Foundry, Azure OpenAI, and Microsoft Agent Framework.
- Experience with LangGraph, Semantic Kernel, MCP, and multi-agent workflows.
- Experience reviewing or enhancing existing production AI solutions.
- Current location and availability to join.
- Expected monthly remuneration for the initial remote engagement and for onsite role.
- Confirmation of willingness to relocate to Abu Dhabi after 2 months, if required.
Please apply only if you have strong hands-on experience developing production-grade AI agents on Azure and are open to potential relocation to Abu Dhabi, UAE.
About Efforts Solutions IT
Efforts Solutions IT is an Abu Dhabi-based technology company established in 2009, delivering Enterprise AI, Intelligent Automation, Data Engineering, Digital Transformation, and IT Outsourcing services to enterprise and government organisations across the UAE, Saudi Arabia, and international markets.
Website: www.effortz.com
Pay: From Rs.1,50,000 per month
Work Location: Remote Initially from Chennai | Abu Dhabi, UAE (Potential Onsite Relocation after 2 months if required)
Pay: ₹150,000.00 - ₹200,000.00 per month
Work Location: In person
