• Develop and deploy at least three (3) production-grade AI/ML models, including use cases such as predictive maintenance, anomaly detection, classification, forecasting, or optimization. • Design, build, and maintain end-to-end machine learning pipelines, including data preprocessing, feature engineering, model training, evaluation, and deployment. • Apply deep expertise in PyTorch and/or TensorFlow to develop and fine-tune advanced machine learning and deep learning models. • Implement and support scalable model serving architectures, ensuring high availability, low latency, and secure inference in production environments. • Collaborate with data engineers to access, transform, and prepare large-scale datasets for model training and inference. • Partner with product owners, analysts, and stakeholders to translate business requirements into machine learning solutions. • Monitor model performance in production, including drift detection, accuracy tracking, and retraining strategies. • Ensure compliance with federal security, privacy, and governance standards in all AI/ML implementations. • Participate in Agile development cycles, including sprint planning, design reviews, and technical demonstrations. • Document model architectures, training methodologies, and deployment processes for maintainability and auditability.

• Translate ambiguous customer questions into well-scoped data science projects spanning panel data, time series, and causal inference• Engineer features from large alternative-data panels (transaction-level, invoice-level, web-scraped) using Spark• Build, validate, and interpret causal and predictive models that link alternative-data signals to financial outcomes such as revenue, earnings surprise, and KPI inflections• Author technical white papers and customer analyses for institutional investors and Fortune 500 readers, including figures, equations, and narrative framing for sophisticated readers• Use LLM coding assistants and agents as a primary collaborator: prototype faster, write higher-quality code, audit your own work, and ship deliverables in compressed timelines• Build internal LLM-driven tooling (agents, eval harnesses, retrieval pipelines) for the broader organization• Partner with data engineering, product, and revenue teams to close the loop between signal development and the customer-facing product• Set technical standards for the team: PEP 8, type hints, vectorized operations, reproducible notebooks, sound methodology, and citation discipline

• 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 • Collaborating with cross-functional teams and business partners to drive current and future strategy by leveraging analytical skills

Role 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. Qualifications - Proficiency in Python is essential. - Expertise in LangChain/LangGraph, CrewAI, Semantic Kernel/Autogen and Open AI Agentic SDK - Experience with TensorFlow, PyTorch, Scikit-learn, and AutoML. - Hands-on experience with generative AI models, RAG (Retrieval-Augmented Generation) architecture, and Natural Language Processing (NLP). - Familiarity with AWS (SageMaker, EC2, S3) and/or Google Cloud Platform (GCP). - Proficiency in data preprocessing and feature engineering. - Experience with GitHub for version control. - Proficiency with development tools such as VS Code and Jupyter Notebook. - Experience with Docker containerization and deployment techniques. - Knowledge of Snowflake and Oracle is a plus. - Familiarity with AWS Bedrock API and/or other GenAI APIs. - Skills in building models, testing/validation, and deployment. - Experience working in an Agile framework. Requirements - Experience with data ingestion, data retrieval, and data generation using optimal methods such as hybrid search. - Knowledge of the insurance industry is a big plus. - Working knowledge of Google Cloud Platform is a plus. - 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. - Exceptional command in Agentic AI architecture, development, testing, and research of both Neural-based & Symbolic agents. - Expertise in building agentic systems using techniques including Multi-agent systems, Reinforcement learning, flexible/dynamic workflows, caching/memory management, and concurrent orchestration. - Expertise in Python language to build large, scalable applications, conduct performance analysis, and tuning. - Strong skills in prompt engineering and its techniques including design, development, and refinement of prompts. - Experience in designing, building, and implementing IR/RAG systems with Vector DB and Knowledge Graph. - Strong skills in the evaluation of models and their tools. - Experience in conducting rigorous A/B testing and performance benchmarking of prompt/LLM variations. 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.