Tiger Analytics - Artificial Intelligence/Machine Learning Engineer

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

As an AI/ML Engineer specializing in Generative AI and NLP, you will be at the forefront of AI innovation. You will design and deploy sophisticated models, focusing on Vertex AI to build Agentic workflows that solve complex business problems. You will work closely with cross-functional teams to create scalable, data-driven solutions that bring AI-driven autonomy and intelligence to life.

Core Responsibilities :

- Agentic AI & NLP Development : Design, develop, and deploy advanced applications using Generative AI models (e.g., Gemini, GPT, LLaMA) and NLP algorithms. Lead the creation of autonomous agents capable of tool-use, reasoning, and multi-step task execution.

- Vertex AI Orchestration : Leverage the Vertex AI platform to build, deploy, and monitor production-ready GenAI applications.

- Model Customization & Fine-Tuning : Apply state-of-the-art techniques like LoRA, PEFT, and fine-tuning to adapt models to specific enterprise use cases.

- Agentic Reasoning & RAG : Implement advanced Retrieval-Augmented Generation (RAG) and agentic frameworks (like LangChain or CrewAI) to ensure agents can access external data and perform actions via API integrations.

- Innovative Problem Solving : Tackle real-world business problems by providing creative, scalable AI-powered solutions that drive measurable results.

- Cross-Functional Collaboration : Partner with Consulting and Engineering to integrate AI agents into broader business strategies, ensuring seamless deployment and high availability.

- Client Engagement : Collaborate with clients to translate their needs into tailored AI solutions, educating them on the potential of Agentic AI to transform operations.

What Do We Expect?

- Agentic Framework Mastery : Proven ability to design and build scalable LLM-based applications using agentic design patterns (Reasoning/Act, Planning, and Tool-Use).

- GCP Expertise : Hands-on experience with Google Cloud Platform (GCP), specifically Vertex AI, including managing datasets, training pipelines, and endpoint deployment.

- Model Performance & Safety : Ability to address issues like bias and hallucinations by implementing robust guardrails and evaluation frameworks (e.g., Vertex AI Model Evaluation).

- Deep Research Application : In-depth understanding of LLM architectures and the ability to apply cutting-edge research to solve complex problems.

- Production-Level Python : Expert-level Python skills for complex data manipulation and large-scale model optimization.

- Advanced Data Stack : Capability to develop sophisticated visualizations and ML models using libraries like Plotly and Scikit-learn, while writing clean, production-level code.

- Communication Skills : Exceptional ability to translate technical AI concepts (like agentic reasoning or vector embeddings) into business insights for non-technical stakeholders.