Job Title &

- Summary

Data Scientist

Drive cutting-edge research and development in autonomous AI agents, Agentic AI, and GenAI systems to power next-gen airline solutions.

Role Summary

- Design, build, and scale production‑grade AI systems, including autonomous AI agents, for Lab37’s innovation and enterprise AI platforms.
- Work on Agentic AI, GenAI, Knowledge Graph–driven systems, and data intelligence layers, leveraging Microsoft Foundry as the core AI platform, across customer, operations, and commercial airline use cases.
- Contribute end‑to‑end—from problem discovery and prototype to enterprise‑scale production deployment.

Key Responsibilities

- Design and implement autonomous AI systems including decision agents, workflow agents, and self‑orchestrating AI components.
- Build and manage knowledge graphs and data intelligence layers to enable contextual reasoning, semantic search, and intelligent decision‑making.
- Develop and deploy AI solutions using Microsoft Foundry for model lifecycle management, orchestration, and enterprise integration.
- Develop Agentic AI solutions that leverage structured and unstructured data for real‑time insights.
- Design and implement AI/ML and GenAI solutions aligned with Lab37’s roadmap and airline business priorities.
- Build robust APIs and backend services for AI models using Python and FastAPI.
- Integrate AI solutions with enterprise systems, internal platforms, and cloud services.
- Ensure scalability, reliability, security, governance, and cost efficiency of AI services.
- Collaborate with product, data, and platform teams to move ideas from POC →
- MVP →
- Production.
- Required Skills / Must-Have

Programming: Solid proficiency in Python. AI / ML: Hands‑on experience with autonomous AI, Agentic AI systems, LLMs, and GenAI.





Microsoft AI Ecosystem: Strong hands‑on experience across the Microsoft AI ecosystem, including Microsoft Foundry, Azure AI Services, Azure OpenAI, and Azure ML.

Knowledge Graphs &

- Data Intelligence: Experience working with knowledge graphs, embeddings, semantic layers, and data intelligence platforms.

Frameworks &
- Tools: FastAPI, LangChain or equivalent GenAI frameworks.

Cloud: Practical experience with Microsoft Azure (Azure ML, Azure AI services preferred).

APIs &

- Integration: REST APIs, event‑driven or microservices‑based architectures.

- Nice-to-Have / Preferred Skills
- Experience with airline industry datasets or operational systems.
- Familiarity with Reinforcement Learning (RL) and multi-agent systems.
- Knowledge of MLOps practices and CI/CD for ML workflows.
- Contributions to open-source AI projects or publications in top-tier conferences.
- Education &
- Qualifications
- Bachelor’s in Engineering or Mathematics with robust AI/ML experience.
- Hands‑On Expectation
- This is a hands‑on engineering role. Candidates are expected to actively design, build, test, deploy, and support AI solutions end‑to‑end.
- The role requires practical, production‑level experience across the Microsoft AI ecosystem, including Microsoft Foundry, Azure AI services, and model integration into enterprise systems.
- Candidates must be comfortable working on real codebases, real data, and real production constraints, not limited to research, advisory, or high‑level design roles.
- Profiles focused only on theoretical AI, presentations, or vendor‑driven implementations without hands‑on ownership are not suitable for this role.
- Strong ownership mindset is expected—from POC →
- MVP → scalable production deployment.
- Location &
- Work Mode

Gurgaon –
- Work from office

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