About the RoleatQor is hiring an AI Technical Lead to architect and build the next generation of AI agents and intelligent solutions for our customers. This is a hands-on technical leadership role you will design, code, review, and ship production-grade agentic systems while setting the technical bar for the broader AI engineering team.You will be the deepest AI technical voice in the room in customer architecture sessions, internal design reviews, and the codebase itself. Reports directly to the Head of Delivery.

What Youll Do

- Design and build AI agents single-agent, multi-agent, and human-in-the-loop systems for enterprise use cases.
- Architect end-to-end GenAI solutions RAG, agentic RAG, tool-using agents, structured outputs, evaluation pipelines, and guardrails.
- Own the technical reference architecture for AI engagements model selection, orchestration framework, memory, retrieval, observability, and cost optimization.
- Write production code. Lead by example in the IDE agent loops, tool definitions, prompt scaffolding, evaluation harnesses, and integration layers.
- Lead code and design reviews across the AI team; raise the bar on quality, testability, and reliability.
- Drive prompt engineering and evaluation as engineering disciplines versioning, regression testing, offline/online eval, and quality metrics.
- Mentor AI engineers and developers transitioning into agentic and GenAI work.
- Partner with pre-sales on solution design, POCs, and technical demos for strategic deals.
- Stay on the frontier evaluate recent models, frameworks, and patterns; bring the right ones into atQors stack.

Core Technical Skills (Must-Have) Languages & Engineering Foundations
- Python (primary) strong proficiency in modern Python, async patterns, type hints, packaging, testing.
- TypeScript / Node.js for agent UIs, tool servers, and Microsoft 365 / Copilot extensibility.
- Solid grounding in REST/GraphQL APIs, async messaging,



microservices, and event-driven architectures.
- Comfort with Git, CI/CD, containerization (Docker), and IaC (Bicep / Terraform).

LLMs & Foundation Models
- Hands-on with Azure OpenAI, OpenAI, Anthropic Claude, and open-source models (Llama, Mistral, Phi).
- Deep understanding of tokenization, context windows, function/tool calling, structured outputs, streaming, and caching.
- Experience with fine-tuning, distillation, and model evaluation (helpful, not mandatory).

Agent Frameworks & Orchestration
- Production experience with at least two of: LangChain / LangGraph, Semantic Kernel, AutoGen, CrewAI, Microsoft Agent Framework, Pydantic AI.
- Hands-on with Microsoft 365 Copilot extensibility declarative agents, custom engine agents, Copilot Studio.
- Experience designing multi-agent systems supervisor/worker, planner/executor, debate, and tool-routing patterns.
- Familiarity with MCP (Model Context Protocol) and modern tool/function-calling standards.

Retrieval & Knowledge Systems
- Strong RAG fundamentals chunking, embeddings, hybrid search, reranking, query rewriting.
- Hands-on with Azure AI Search, vector DBs (pgvector, Pinecone, Weaviate, Qdrant), and graph-based retrieval.
- Experience with Microsoft Fabric / OneLake, SharePoint, and Microsoft Graph as enterprise knowledge sources.

Evaluation, Safety & Observability
- Built and run evaluation pipelines golden sets, LLM-as-judge, regression testing, A/B evaluation.
- Familiar with tools like Azure AI Foundry evaluations, LangSmith, Langfuse, Promptfoo,



Ragas.
- Working knowledge of AI safety, prompt injection defenses, PII handling, content filtering, and responsible AI practices.

Cloud & Platform
- Strong Microsoft Azure depth App Service, Functions, AKS, API Management, Key Vault, Cosmos DB, Service Bus.
- Familiarity with Azure AI Foundry, Azure ML, and AI Hub for model lifecycle management.
- DevOps for AI prompt versioning, model deployment, telemetry, cost monitoring, and rollback strategies.

Nice-to-Have
- Experience with voice agents, multimodal models (vision, audio), or browser-using agents.
- Background in classical ML / NLP embeddings, classifiers, NER, topic modeling.
- Open-source contributions to AI frameworks or published technical writing.
- Experience integrating AI with SharePoint, Power Platform, Dynamics 365, or Teams.

What You Bring
- 8 12 years in software engineering, with at least 3 years building production GenAI / agentic systems.
- Deep hands-on experience this is not a managerial role; you will be in code reviews and on the keyboard.
- Strong system design skills can take an ambiguous customer problem to a working agent in days, not months.
- Excellent technical communication can explain a multi-agent architecture to a CTO and a junior engineer in the same meeting.
- Bias for shipping comfortable with the trade-offs of getting reliable AI into production.

Education & Certifications
- Bachelors or Masters in Computer Science, Engineering, or related field.
- Certifications preferred: Azure AI Engineer Associate, Azure Solutions Architect Expert, Databricks ML Professional.

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.

📌 Tech Lead - AI (Ahmedabad)
🏢 atQor
📍 Ahmedabad