Tiger Analytics - Full Stack Engineer - Generative AI Applications

Curious about the role ? What your typical day would look like ?

We are seeking a highly technical, visionary, and hands-on Leaders to champion our full-stack AI Centers 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.

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 modeling 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 modern UI engineering patterns for AI interactions.

- Infrastructure & MLOps Mastery :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.