AI and Agent Engineer - Senior Associate

Weaver-and-Tidwell-India-LLP; Tamil Nadu; On-site; INR 1,800,000 - 3,000,000; Full time

Job summary

Weaver-and-Tidwell-India-LLP in India seeks a Senior AI and Agent Engineer to develop, integrate, test, deploy, and monitor AI-enabled applications and agents across the Microsoft ecosystem. This is an AI application engineering role focused on production software, enterprise-system integration, and robust evaluation frameworks.

You will build Copilot Studio agents, implement retrieval-augmented generation, and design agent behavior with escalation rules while ensuring efficient, scalable

Qualifications

• 5+ years of software, integration, automation, or data application development.
• Production experience with generative AI or conversational AI applications.
• Experience across Copilot Studio, Power Platform, and Azure.
• Familiarity with Semantic Kernel or comparable orchestration frameworks.
• Basic C# experience within the Microsoft ecosystem.
• Experience in regulated, confidential, or professional-services environments.

Responsibilities

• Develop Copilot Studio agents, topics, actions, triggers, workflows, and generative orchestration.
• Implement retrieval-augmented generation, structured outputs, tool use, multi-step orchestration, and human approval patterns.
• Develop custom APIs, Azure Functions, connectors, and supporting application services.
• Integrate AI solutions with Microsoft Graph, SharePoint, Teams, Dataverse, Power Platform, and approved enterprise systems.
• Design system instructions, prompts, grounding logic, response structures, and escalation rules.
• Maintain version control for prompts, instructions, tools, actions, and model configurations.
• Automate tests for groundedness, relevance, completeness, citation quality, task completion, and escalation behavior.
• Instrument solutions for usage, latency, failures, cost, and model behavior.

Skills

REST APIs

JSON

OAuth

OpenAPI

webhooks

event-driven integration

MS Graph integration

RAG and vector search

Agent orchestration

State management

Tool invocation

Human in the loop

Automated testing

CI/CD

Monitoring and diagnostics

Tools

Azure Functions

Git & PRs

CI/CD pipelines

Power Platform

Azure Monitor

Application Insights

Job description

AI and Agent Engineer - Senior Associate

The Senior AI and Agent Engineer will develop, integrate, test, deploy, and monitor AI-enabled applications and agents across the Microsoft ecosystem. The role is responsible for the intelligence, orchestration, actions, integrations, and behavioral quality of AI solutions.

This is an AI application engineering role, not a prompt-writing position. The successful candidate must be able to build production software, integrate enterprise systems, create evaluation frameworks, troubleshoot complex behavior, and operate deployed solutions.

Primary Responsibilities

AI and agent development

• Build and configure Copilot Studio agents, topics, actions, triggers, workflows, and generative orchestration.
• Implement retrieval-augmented generation, structured outputs, tool use, multistep orchestration, and human approval patterns.
• Develop custom APIs, Azure Functions, connectors, and supporting application services.
• Integrate AI solutions with Microsoft Graph, SharePoint, Teams, Dataverse, Power Platform, and approved enterprise systems.

Prompt, context, and behavior engineering

• Develop system instructions, prompts, grounding logic, response structures, fallback behavior, and escalation rules.
• Implement model selection and routing based on accuracy, risk, latency, and consumption requirements.
• Design agent behavior that distinguishes between answering, recommending, requesting clarification, escalating, and taking action.
• Maintain version control for prompts, instructions, tools, actions, and model configurations.

Testing and evaluation

• Develop evaluation datasets with practice SMEs.
• Automate tests for groundedness, relevance, completeness, citation quality, task completion, response consistency, and escalation behavior.
• Perform adversarial testing involving prompt injection, data leakage, unsupported instructions, excessive agency, and unauthorized actions.
• Build regression tests to detect changes caused by models, prompts, source content, integrations, or application releases.
• Document known limitations and expected failure behavior.

Operations and improvement

• Instrument solutions for usage, latency, failures, cost, task completion, user feedback, and model behavior.
• Review production telemetry and interaction patterns.
• Investigate incidents and implement corrective changes.
• Optimize token consumption, retrieval performance, application latency, and integration reliability.
• Develop reusable agent templates, integration components, evaluation harnesses, and error-handling patterns.

Required Technical Skills

• REST APIs, JSON, OAuth, OpenAPI, webhooks, and event-driven integration.
• Microsoft Graph, Dataverse, SharePoint, and Teams integration.
• Retrieval-augmented generation, embeddings, vector search, semantic search, hybrid search, reranking, and structured generation.
• Agent orchestration, state management, tool invocation, and human-in-the-loop controls.
• Automated testing, Git, pull requests, CI/CD, and production troubleshooting.
• Application Insights, Azure Monitor, logging, alerting, and performance diagnostics.
• Understanding of prompt injection, data exfiltration, overreliance, hallucination, unsafe actions, and AI-specific failure modes.

Preferred Experience

• 5 or more years of software, integration, automation, or data application development.
• Demonstrated production experience with generative AI or conversational AI applications.
• Experience building applications across Copilot Studio, Power Platform, and Azure.
• Familiarity with Semantic Kernel or comparable orchestration frameworks.
• Basic C# experience within the Microsoft ecosystem.
• Experience in regulated, confidential, or professional-services environments.

Success Measures

• Evaluation performance against approved acceptance thresholds.
• Task-completion and appropriate-escalation rates.
• Production defect and regression rates.
• Retrieval and grounding quality.
• Response latency and cost per transaction.
• Integration reliability.
• Reuse of standard components.
• Completeness of technical documentation and automated testing.
• Speed and effectiveness of incident remediation.

This role engineers the behavior of AI applications. Practice SMEs remain accountable for defining and validating authoritative professional content, business rules, acceptable conclusions, and circumstances requiring human review.