Job Summary

Job Type	Full-Time
Experience	4–7 years overall; at least 2 years in Generative AI / Agentic AI with production delivery on Microsoft Azure
Locations	Noida, Hyderabad, Chennai, Bangalore, Pune
Primary Focus	Customer-specific engineering, integrations, deployment, productionization and troubleshooting

Role Overview

We are looking for a Forward Deployment Engineer (FDE) to design, customize, integrate, deploy, troubleshoot, and operationalize enterprise Generative AI and Agentic AI solutions for customers on Microsoft Azure. The FDE is a hands-on customer-facing engineer who turns reusable platform capabilities into working customer solutions and owns the implementation from technical discovery through production go-live and hypercare.

• Hands-on customer implementation engineer
• Heavy software and integration ownership
• Executes the customer deployment plan
• Escalates architecture/platform decisions when required
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Typical time allocation

• ~70% engineering and integration
• ~20% customer interaction
• ~10% architecture contribution

Key Responsibilities

• Work directly with customer technical teams to understand application, infrastructure, data, integration, security, and operational requirements and translate them into implementable solutions.
• Participate in technical discovery sessions, solution workshops, demonstrations, proof-of-concepts, pilots, deployment planning, and go-live activities.
• Implement customer-specific Agentic AI solutions using Microsoft Foundry / Foundry Agent Service, Azure OpenAI models, Python, and appropriate agent frameworks such as LangGraph, LangChain, or Microsoft Agent Framework.
• Configure and customize reusable agents, Skills, tools, workflows, RAG components, prompts, and enterprise connectors for customer environments.
• Develop customer-specific integrations using REST APIs, SDKs, MCP tools, databases, ITSM platforms, Microsoft Graph, monitoring systems, identity services, and proprietary applications when reusable connectors are unavailable.
• Configure customer endpoints, authentication, authorization, OAuth/API credentials, Entra ID identities, RBAC, Key Vault, API Management, and other security controls required for integration.
• Deploy and operate customer solutions on Azure using services such as Foundry Agent Service, Azure Container Apps, AKS, Azure Functions, API Management, Service Bus, Event Grid, Storage, Azure AI Search, and related services.
• Configure customer-specific RAG and knowledge integrations, including data ingestion, retrieval, access control, and validation using services such as Azure AI Search and customer-approved data sources.
• Implement logging, tracing, metrics, monitoring, evaluation, and operational dashboards using Microsoft Foundry observability, Azure Monitor, Application Insights, OpenTelemetry, and related capabilities.
• Execute functional, integration, security, performance, resiliency, evaluation, and user acceptance testing before production rollout.
• Troubleshoot production issues across agents, models, prompts, tools, APIs, RAG, authentication, networking, and Azure infrastructure; drive issues to closure.
• Analyze logs, traces, metrics, evaluation results, latency, token usage, and failure patterns and implement corrective actions.
• Support CI/CD, environment promotion, versioning, releases, rollback, and production deployment activities.
• Optimize solutions for reliability, scalability, latency, usability, and AI inference cost.
• Document customer architecture, integrations, configuration, deployment steps, operational runbooks, troubleshooting procedures, and support information.
• Conduct knowledge transfer to customer teams and internal support/BAU teams and support production hypercare.
• Identify recurring customer requirements and provide structured feedback to Agent Development / Platform Engineering teams for reusable platform improvements.

Skill Requirements

Preferred Skills

• Experience with Microsoft Foundry, Foundry Agent Service, Azure OpenAI, Azure AI Search, Azure Monitor, Application Insights, and GenAI evaluation.
• Experience with MCP, Microsoft Graph, and enterprise tool integration.
• Experience with Terraform / Infrastructure-as-Code.
• Experience with AKS, Azure Container Apps, API Management, Service Bus, Event Grid, Azure Storage, Key Vault, Cosmos DB, networking, private endpoints, and private connectivity.
• Experience with ServiceNow or other ITSM platforms and IT infrastructure / CloudOps / SRE / AIOps use cases.
• Experience supporting customer POCs, pilots, MVPs, production rollouts, and technical workshops.

Must Have Skills

• Strong Python programming and software engineering fundamentals.
• Hands-on experience with LLMs, RAG, prompt engineering, agentic workflows, tool/function calling, structured outputs, and multi-agent systems.
• Hands-on experience with Microsoft Foundry / Foundry Agent Service, Azure OpenAI, and/or production agent frameworks.
• Strong REST API development and integration experience; comfortable working with JSON, YAML, SDKs, Microsoft Graph, and enterprise APIs.
• Working knowledge of Entra ID, OAuth, API keys, Azure RBAC, managed identities, service principals, secrets management, authentication, and authorization.
• Hands-on experience with Azure and at least one production deployment model using Azure Container Apps or AKS.
• Experience with Git, CI/CD, Docker, and production deployment practices.
• Experience with logging, monitoring, tracing, debugging, and production incident troubleshooting.
• Good understanding of databases, SQL, enterprise data sources, and RAG implementation patterns.
• Good understanding of Responsible AI, security, data protection, and enterprise deployment requirements.
• Strong communication skills and ability to work directly with customer technical stakeholders.

 

Other Requirements

Qualifications

• Bachelor’s or Master’s degree in Computer Science, Engineering, Information Technology, or related field.
• 4–7 years of overall software engineering, cloud engineering, or equivalent technical experience.
• At least 2 years of practical Generative AI / Agentic AI experience.
• Demonstrated experience delivering an AI, automation, or cloud solution into production.

Key Attributes

• Customer-oriented and comfortable working in customer environments.
• Strong ownership and bias for execution.
• Strong troubleshooting and problem-solving ability.
• Able to balance reusable platform capabilities with customer-specific requirements.
• Comfortable working across AI engineering, Azure cloud, integration, and operations.