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Curious about the role?

We are seeking a highly technical, visionary, and hands-on Leaders to champion our full-stack AI Centres of Excellence (CoEs). In this leadership role, you will sit at the intersection of advanced AI solutioning, hyperscaler ecosystems (specifically GCP/AWS/Azure), and robust full-stack engineering.

You will be responsible for designing and deploying end-to-end Agentic AI systems, bringing both deep technical execution and executive-level solutioning to our clients. If you are passionate about moving beyond simple wrappers to engineer robust, enterprise-grade cognitive architectures, this role is for you.

What your typical day would look like?

As part of this role, you will :

- Drive Hyperscaler CoE Innovation: Architect and scale full-stack GenAI solutions heavily integrated with hyperscaler ecosystems, with a deep focus on GCP (Vertex AI), Anthropic (Claude/Cortex), and adjacent modern AI stacks.
- Architect Agentic Systems: Design and build autonomous, end-to-end Agentic AI systems, multi-agent orchestrations, and production-grade RAG pipelines capable of solving complex corporate workflows.
- Lead Full-Stack AI Engineering: Oversee the end-to-end development lifecycle, from sophisticated prompt engineering and LLM fine-tuning (LoRA, PEFT) to robust backend microservices and responsive application layers.
- Ensure Enterprise Deployment &
- MLOps: Bridge the gap between AI modelling and core infrastructure by owning cloud scalability, containerization, robust error management, and automated CI/CD/MLOps monitoring pipelines.
- Deliver Strategic Solutioning:



Partner closely with senior leadership and client stakeholders to translate intricate business challenges into scalable, production-ready AI blueprints.
- Mentor and Build Elite Teams: Cultivate, mentor, and scale a high-performing team of AI full-stack engineers and data scientists, fostering a culture of technical excellence and aggressive innovation.

Who do we expect?

- Experience: 15 years of technical engineering experience, with at least 4 years of dedicated leadership in leading NLP, Deep Learning, and Generative AI applications.
- Hyperscaler &
- AI Ecosystem Depth: Extensive hands-on experience with GCP (Vertex AI, BigQuery, GKE), Anthropic model suites, and deep familiarity with frameworks like LangChain, LlamaIndex, or CrewAI/Autogen for agentic setups.
- Core GenAI Skills: Proven expertise in building production RAG frameworks, advanced prompt optimization, and applying parameter-efficient fine-tuning techniques (LoRA, PEFT).
- Strong background in full-stack architecture, featuring elite Python skills, API/Microservices design (FastAPI, Django, or Flask), and exposure to up-to-date UI engineering patterns for AI interactions.
- Infrastructure &
- MLOps: Proficient with Docker, Kubernetes, and automated CI/CD infrastructure, ensuring reliable scaling, low latency, and robust mitigation against LLM vulnerabilities (hallucinations, security bias).
- Data Engineering Literacy: Solid fundamentals in distributed computing, vector databases (e.g., Pinecone, Milvus, pgvector), and cloud data warehouse ecosystems.
- Leadership &
- Influence: Strong executive presence, capable of fluidly pivoting from technical deep-dives with engineers to strategic product roadmap discussions with enterprise buyers.

📌 Tiger Analytics - AI Engineer (India)
🏢 Tiger Analytics
📍 India