Agentic AI Engineer

April 15

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Tiger Analytics

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Tiger Analytics is a leading AI and analytics consulting firm that specializes in leveraging data science and machine learning to provide strategic business insights across various industries. They offer services in data strategy, AI engineering, and business intelligence to enable data-driven decision-making and digital transformation for their clients. Tiger Analytics collaborates with top technology partners like Microsoft, Google Cloud, and AWS to deliver cutting-edge solutions. They serve a diverse range of sectors including consumer packaged goods, healthcare, and finance, helping businesses operationalize insights and differentiate with AI and machine learning technologies.

Machine Learning • Predictive Analytics • Forecasting • Optimization • Natural Language Processing

1001 - 5000 employees

Founded 2011

🤖 Artificial Intelligence

🤝 B2B

📋 Description

• Tiger Analytics is looking for experienced Agentic AI Engineer with Gen AI experience to join our fast-growing advanced analytics consulting firm. • 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.

🎯 Requirements

• Programming Languages: Proficiency in Python is essential; C++ experience is ideal but not required. • 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. • 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. • 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.

🏖️ Benefits

• 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.