Agentic AI Engineer

Tiger Analytics is looking for experienced Agentic AI Engineer with Gen AI experience to join our fast-growing advanced analytics consulting firm. Our employees bring deep expertise in Machine Learning, Data Science, and AI. We are the trusted analytics partner for multiple Fortune 500 companies, enabling them to generate business value from data. Our business value and leadership has been recognized by various market research firms, including Forrester and Gartner.

We are looking for top-notch talent as we continue to build the best global analytics consulting team in the world. You will be responsible for:

• Providing solutions for the deployment, execution, validation, monitoring, and improvement of MLE solutions
• Creating Scalable Machine Learning systems .
• Building reusable production data pipelines for implemented machine learning models
• Writing production-quality code and libraries that can be packaged as containers, installed and deployed

You will collaborate with cross-functional teams and business partners and will have the opportunity to drive current and future strategy by leveraging your analytical skills as you ensure business value and communicate the results.

Technical Skills Required:

·        Programming Languages: Proficiency in Python is essential.

·        Agentic AI : Expertise in LangChain/LangGraph, CrewAI, Semantic Kernel/Autogen and Open AI Agentic SDK

·        Machine Learning Frameworks: Experience with TensorFlow, PyTorch, Scikit-learn, and AutoML.

·        Generative AI: Hands-on experience with generative AI models, RAG (Retrieval-Augmented Generation) architecture, and Natural Language Processing (NLP).

·        Cloud Platforms: Familiarity with AWS (SageMaker, EC2, S3) and/or Google Cloud Platform (GCP).

·        Data Engineering: Proficiency in data preprocessing and feature engineering.

·        Version Control: Experience with GitHub for version control.

·        Development Tools: Proficiency with development tools such as VS Code and Jupyter Notebook.

·        Containerization: Experience with Docker containerization and deployment techniques.

·        Data Warehousing: Knowledge of Snowflake and Oracle is a plus.

·        APIs: Familiarity with AWS Bedrock API and/or other GenAI APIs.

·        Data Science Practices: Skills in building models, testing/validation, and deployment.

·        Collaboration: Experience working in an Agile framework.

Desired Skills:

·        RAG Architecture: Experience with data ingestion, data retrieval, and data generation using optimal methods such as hybrid search.

·        Insurance/Financial Domain: Knowledge of the insurance industry is a big plus.

·        Google Cloud Platform: Working knowledge is a plus.

Additional Expertise:

·        Industry Experience: 8+ years of industry experience in AI/ML and data engineering, with a track record of working in large-scale programs and solving complex use cases using GCP AI Platform/Vertex AI.

·        Agentic AI Architecture: Exceptional command in Agentic AI architecture, development, testing, and research of both Neural-based & Symbolic agents, using current-generation deployments and next-generation patterns/research.

·        Agentic Systems: Expertise in building agentic systems using techniques including Multi-agent systems, Reinforcement learning, flexible/dynamic workflows, caching/memory management, and concurrent orchestration. Proficiency in one or more Agentic AI frameworks such as LangGraph, Crew AI, Semantic Kernel, etc.

·        Python Proficiency: Expertise in Python language to build large, scalable applications, conduct performance analysis, and tuning.

·        Prompt Engineering: Strong skills in prompt engineering and its techniques including design, development, and refinement of prompts (zero-shot, few-shot, and chain-of-thought approaches) to maximize accuracy and leverage optimization tools.

·        IR/RAG Systems: Experience in designing, building, and implementing IR/RAG systems with Vector DB and Knowledge Graph.

·        Model Evaluation: Strong skills in the evaluation of models and their tools. Experience in conducting rigorous A/B testing and performance benchmarking of prompt/LLM variations, using both quantitative metrics and qualitative feedback.

This position offers an excellent opportunity for significant career development in a fast-growing and challenging entrepreneurial environment with a high degree of individual responsibility.