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What you’ll work on

Full posting

Design, build and deploy AI agents and multi-agent systems on Azure
Build enterprise RAG solutions with Azure AI Search and vector search
Integrate AI workflows with Microsoft 365, Teams, SharePoint and Dynamics 365
Establish observability, guardrails and monitoring for production AI apps
Optimize agent and LLM applications for latency, accuracy, cost and security

From the employer’s posting

Design, build, and deploy AI agents and multi-agent systems using Azure AI services, Azure OpenAI

Implement enterprise RAG solutions utilizing Azure AI Search, vector search, hybrid search, and semantic ranking

Develop and operationalize agentic workflows for business functions, integrating AI solutions with Microsoft 365, Teams, SharePoint, Dynamics 365, Power Platform

Establish observability, guardrails, responsible AI controls, evaluation, security, and monitoring for AI applications in production environments

Optimize agent and LLM applications for latency, accuracy, reliability, scalability, security, and cost

What you’ll bring

All qualifications

Core experience

Hands-on experience with Azure AI services, Azure OpenAI, and Azure AI Foundry
Python programming for AI/ML solutions
Expertise in agentic frameworks (Semantic Kernel, LangChain, LangGraph, AutoGen)

Preferred experience

Experience with Azure Functions, Azure Container Apps, Azure Kubernetes Service (AKS), and Azure Storage
Bachelor's degree in Computer Science, Data Science, Statistics, Mathematics, or a closely related discipline
Proficiency in R and R Studio for statistical computing
Knowledge of SAS or SPSS for advanced statistical modeling

Qualification wording

Hands-on experience with Azure AI services, Azure OpenAI, and Azure AI Foundry

Python programming for AI/ML solutions

Expertise in agentic frameworks (Semantic Kernel, LangChain, LangGraph, AutoGen)

Experience with Azure Functions, Azure Container Apps, Azure Kubernetes Service (AKS), and Azure Storage

Bachelor's degree in Computer Science, Data Science, Statistics, Mathematics, or a closely related discipline

Proficiency in R and R Studio for statistical computing

Knowledge of SAS or SPSS for advanced statistical modeling

Education & alternatives

Desired Qualifications: - Bachelor's degree in Computer Science, Data Science, Statistics, Mathematics, or a closely related discipline - Certification in Machine Learning or Data Science from a recognized institution (e.g., TensorFlow Developer Certificate, AWS Certified Machine Learning Specialty, Microsoft Certified: Azure AI Engineer Associate)

Tools in this posting

Python
R
SAS
AWS
Azure
Kubernetes
Keras
PyTorch
TensorFlow

Source

- Integration with Microsoft 365, Teams, SharePoint, Dynamics 365, Power Platform, and APIs - Python programming for AI/ML solutions - ML frameworks (TensorFlow, PyTorch, Sci-Kit Learn, Keras)

- Experience with Azure Functions, Azure Container Apps, Azure Kubernetes Service (AKS), and Azure Storage - Proficiency in R and R Studio for statistical computing - Knowledge of SAS or SPSS for advanced statistical modeling

- Proficiency in R and R Studio for statistical computing - Knowledge of SAS or SPSS for advanced statistical modeling - Familiarity with microservices, event-driven architectures, and enterprise integrations

- Certification in Machine Learning or Data Science from a recognized institution (e.g., TensorFlow Developer Certificate, AWS Certified Machine Learning Specialty, Microsoft Certified: Azure AI Engineer Associate) - Certification in statistical analysis tools or platforms (e.g., SAS Certified Statistical Business Analyst) Employment type

- Bachelor's degree in Computer Science, Data Science, Statistics, Mathematics, or a closely related discipline - Certification in Machine Learning or Data Science from a recognized institution (e.g., TensorFlow Developer Certificate, AWS Certified Machine Learning Specialty, Microsoft Certified: Azure AI Engineer Associate) - Certification in statistical analysis tools or platforms (e.g., SAS Certified Statistical Business Analyst)

Job requirements Experience Range: With at least 4 to 6 years of experience designing, building, and operationalizing enterprise-scale AI platforms, including hands-on expertise in agentic AI, LLMs, and Azure-based solutions. Key Responsibilities: - Design, build, and deploy AI agents and multi-agent systems using Azure AI services, Azure OpenAI, and modern agentic frameworks such as Semantic Kernel, LangChain, LangGraph, and AutoGen.

Experience Range: With at least 4 to 6 years of experience designing, building, and operationalizing enterprise-scale AI platforms, including hands-on expertise in agentic AI, LLMs, and Azure-based solutions. Key Responsibilities: - Design, build, and deploy AI agents and multi-agent systems using Azure AI services, Azure OpenAI, and modern agentic frameworks such as Semantic Kernel, LangChain, LangGraph, and AutoGen. - Develop and operationalize agentic workflows for business functions, integrating AI solutions with Microsoft 365, Teams, SharePoint, Dynamics 365, Power Platform, and enterprise APIs.

- Develop and operationalize agentic workflows for business functions, integrating AI solutions with Microsoft 365, Teams, SharePoint, Dynamics 365, Power Platform, and enterprise APIs. - Implement enterprise RAG solutions utilizing Azure AI Search, vector search, hybrid search, and semantic ranking to enhance knowledge retrieval and business decision-making. - Build production-ready AI applications leveraging LLMs, RAG pipelines, tool/function calling, memory systems, and workflow orchestration for scalable, secure, and reliable enterprise deployment.

Required Skills: - Hands-on experience with Azure AI services, Azure OpenAI, and Azure AI Foundry - Expertise in agentic frameworks (Semantic Kernel, LangChain, LangGraph, AutoGen)

- Model evaluation and observability tools (Evidently AI, Great Expectations) - AI security and governance on Azure Preferred Skills:

Preferred Skills: - Experience with Azure Functions, Azure Container Apps, Azure Kubernetes Service (AKS), and Azure Storage - Proficiency in R and R Studio for statistical computing

- Python programming for AI/ML solutions - ML frameworks (TensorFlow, PyTorch, Sci-Kit Learn, Keras) - AI workflow orchestration tools (KubeFlow, BentoML)

Posting record

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Status in our records: Live
First seen by us: Oct 8, 2026 · 10d ago
Recorded sightings: 37
Last seen by us: Oct 17, 2026

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Job description

View original posting ↗

Lead AI/ML Engineer

Job requirements

Experience Range: With at least 4 to 6 years of experience designing, building, and operationalizing enterprise-scale AI platforms, including hands-on expertise in agentic AI, LLMs, and Azure-based solutions. Key Responsibilities:

Design, build, and deploy AI agents and multi-agent systems using Azure AI services, Azure OpenAI, and modern agentic frameworks such as Semantic Kernel, LangChain, LangGraph, and AutoGen.
Develop and operationalize agentic workflows for business functions, integrating AI solutions with Microsoft 365, Teams, SharePoint, Dynamics 365, Power Platform, and enterprise APIs.
Implement enterprise RAG solutions utilizing Azure AI Search, vector search, hybrid search, and semantic ranking to enhance knowledge retrieval and business decision-making.
Build production-ready AI applications leveraging LLMs, RAG pipelines, tool/function calling, memory systems, and workflow orchestration for scalable, secure, and reliable enterprise deployment.
Establish observability, guardrails, responsible AI controls, evaluation, security, and monitoring for AI applications in production environments.
Optimize agent and LLM applications for latency, accuracy, reliability, scalability, security, and cost, applying advanced techniques and metrics.
Collaborate with cross-functional teams to identify AI opportunities, rapidly prototype solutions, and iterate based on user feedback.
Contribute to internal best practices around agent architecture, prompting, RAG, model selection, evaluation, and AI engineering standards.

Required Skills:

Hands-on experience with Azure AI services, Azure OpenAI, and Azure AI Foundry
Expertise in agentic frameworks (Semantic Kernel, LangChain, LangGraph, AutoGen)
LLM application development and prompt engineering
Enterprise RAG architectures and vector search technologies
Integration with Microsoft 365, Teams, SharePoint, Dynamics 365, Power Platform, and APIs
Python programming for AI/ML solutions
ML frameworks (TensorFlow, PyTorch, Sci-Kit Learn, Keras)
AI workflow orchestration tools (KubeFlow, BentoML)
Model evaluation and observability tools (Evidently AI, Great Expectations)
AI security and governance on Azure

Preferred Skills:

Experience with Azure Functions, Azure Container Apps, Azure Kubernetes Service (AKS), and Azure Storage
Proficiency in R and R Studio for statistical computing
Knowledge of SAS or SPSS for advanced statistical modeling
Familiarity with microservices, event-driven architectures, and enterprise integrations
Expertise in developing reusable agent frameworks, prompt libraries, and deployment patterns

Desired Qualifications:

Bachelor's degree in Computer Science, Data Science, Statistics, Mathematics, or a closely related discipline
Certification in Machine Learning or Data Science from a recognized institution (e.g., TensorFlow Developer Certificate, AWS Certified Machine Learning Specialty, Microsoft Certified: Azure AI Engineer Associate)
Certification in statistical analysis tools or platforms (e.g., SAS Certified Statistical Business Analyst)

Employment type

Employee

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Source details

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Pay: No pay amount identified in the saved description.
Location & working pattern: Bangalore, Karnataka, India
- Develop and operationalize agentic workflows for business functions, integrating AI solutions with Microsoft 365, Teams, SharePoint, Dynamics 365, Power Platform, and enterprise APIs. - Implement enterprise RAG solutions utilizing Azure AI Search, vector search, hybrid search, and semantic ranking to enhance knowledge retrieval and business decision-making. - Build production-ready AI applications leveraging LLMs, RAG pipelines, tool/function calling, memory systems, and workflow orchestration for scalable, secure, and reliable enterprise deployment.
Work authorization: No clear work-authorization passage found. Eligibility is unconfirmed.

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