Senior AI Engineer & Microsoft copilot studio

CittaAI; Hyderabad; On-site; INR 1,800,000 - 2,400,000; Full time

Job summary

CittaAI is seeking a Senior AI Engineer to design and deploy enterprise Generative AI applications, including copilots and agents, across LLMs, RAG, tool calling, and Microsoft Copilot Studio. You will own architecture and production deployment, collaborating with cross‑functional teams.

The role emphasizes security, observability, scalability, and cost efficiency, with hands‑on implementation in Python, FastAPI, React/Next.js, and cloud platforms, delivering robust AI solutions.

Qualifications

• 4 to 7+ years of software/AI engineering experience.
• Hands-on experience building production Generative AI applications.
• Strong skills in FastAPI, REST APIs, and distributed architectures.
• Experience with Copilot Studio and enterprise AI integrations.

Responsibilities

• Design and build enterprise-grade AI applications including copilots and agents.
• Develop end-to-end RAG pipelines for document processing and retrieval.
• Create AI agent systems with tool calling, memory, and fault tolerance.
• Integrate LLMs and services using Python and FastAPI.
• Collaborate with DevOps for deployment and observability.

Skills

Generative AI

Python

FastAPI

LLMs

Copilot Studio

LangChain

Azure

CI/CD

Observability

Tools

Git

Docker

Kubernetes

LangChain

CI/CD pipelines

Job description

Job Title: Senior AI Engineer & Microsoft copilot studio

Experience: 4 to 7+ Years

Employment Type: Full-time

We are looking for a Senior AI Engineer with strong hands‑on experience building and deploying enterprise Generative AI applications/agents and Microsoft Copilot Studio solutions.

You will work across LLM applications, RAG, AI agents, tool calling, enterprise copilots, Microsoft Copilot Studio, Python/FastAPI services, integrations, evaluation, security, and observability.

This is a senior individual contributor role where you will own AI solutions from architecture and design through production deployment and operations. You will make engineering trade-offs across quality, latency, scalability, security, reliability, and cost, while establishing reusable patterns for the wider engineering team.

Responsibilities:

• Design and build enterprise‑grade AI applications including copilots, knowledge assistants, and agentic workflows.
• Build RAG pipelines end to end - document ingestion, preprocessing, chunking, embeddings, retrieval, hybrid search, reranking, and context construction.
• Develop AI agent systems with tool calling, state management, memory, human‑in‑the‑loop workflows, and failure recovery.
• Integrate LLMs and AI services into production applications using Python and FastAPI.
• Work with LangChain, LangGraph, or equivalent AI orchestration frameworks.
• Build reusable AI engineering patterns and components for enterprise applications.
• Design, develop, and deploy enterprise copilots using Microsoft Copilot Studio.
• Build conversational agents, topics, actions, workflows, and enterprise integrations.
• Integrate Copilot Studio with Microsoft 365, Power Platform, Dataverse, APIs, and external enterprise systems.
• Configure and implement authentication, permissions, connectors, and secure access to enterprise data.
• Build custom actions and integrations using APIs, Power Automate, and other Microsoft services.
• Implement grounding and knowledge sources to improve response accuracy and reliability.
• Evaluate and optimize copilots for response quality, latency, usability, and business outcomes.
• Understand and apply responsible AI, security, governance, and access‑control practices within Microsoft environments.
• Build backend APIs and services using Python and FastAPI.
• Develop supporting application interfaces using React and/or Next.js.
• Design distributed services and integrations for enterprise AI applications.
• Implement authentication and authorization using OAuth2, OIDC, RBAC, and enterprise identity platforms where required.

Evaluation, LLMOps & Observability

• Define evaluation frameworks for groundedness, relevance, accuracy, latency, reliability, and cost.
• Build automated evaluation and regression‑testing pipelines for LLM applications.
• Implement telemetry and observability for AI applications and agents.
• Monitor production AI systems for failures, latency, token usage, cost, and model behavior.
• Establish processes for continuous improvement, evaluation, and model/application drift.
• Own deployment and production operations for AI applications.
• Work with Azure, containers, CI/CD, and cloud‑native architectures.
• Implement automated testing, deployment, monitoring, debugging, and incident response.
• Collaborate with DevOps/platform teams on production infrastructure and reliability.

Security & Governance

• Implement secure AI application architectures for enterprise environments.
• Apply authentication, authorization, RBAC, and secure API access.
• Understand and mitigate prompt injection, data leakage, insecure tool usage, and sensitive‑data exposure.
• Implement appropriate logging, auditing, and governance mechanisms for AI systems.
• Follow responsible AI and enterprise security practices.

Leadership

• Lead technical design and architecture discussions.
• Conduct design and code reviews.
• Mentor engineers and establish engineering best practices.
• Convert ambiguous business requirements into scalable and shippable AI solutions.
• Work closely with product, business, security, and engineering teams.

Required Skills

• 4 to 7+ years of software engineering / AI engineering experience.
• Hands‑on experience building and deploying production Generative AI applications.
• Strong experience with FastAPI, REST APIs, and distributed application architecture.
• Strong understanding of LLMs, RAG, Embeddings, Vector search, Hybrid search, AI Agents, Tool calling, Prompt engineering, LLM evaluation.
• Hands‑on experience with Microsoft Copilot Studio.
• Experience building conversational agents, actions, workflows, and enterprise integrations using Copilot Studio.
• Experience with Power Platform / Power Automate / Dataverse is highly valuable.
• Experience with LangChain, LangGraph, or equivalent orchestration frameworks.
• Experience with vector databases and semantic search.
• Strong understanding of Git, CI/CD, containers, automated testing, and production debugging.
• Experience working with Azure or another major cloud platform.

Nice to have

• Microsoft Copilot Studio advanced capabilities.
• Microsoft Power Platform.
• Microsoft 365 / Graph API.
• OAuth2 / OIDC / RBAC.
• Pinecone, Weaviate, pgvector, or other vector databases.
• Azure DevOps or GitHub Actions.
• LLMOps and evaluation frameworks.
• AI observability platforms.
• React and/or Next.js.
• Experience implementing enterprise AI governance and security.
  

Key Skills

What You'll Build

You will work on production‑grade solutions such as:

• RAG‑based knowledge assistants
• AI‑powered business workflows
• Multi‑agent systems
• Enterprise API/tool integrations
• AI‑powered automation
• LLM evaluation and observability platforms