AI Engineer

🕒 Maio 1

🇺🇸 Estados Unidos – Remoto (EUA)

⏰ Tempo Integral

🟠 Sênior

🔴 Especialista

🤖 Engenheiro de IA

🦅 Patrocina Visto H1B

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Tiger Analytics

SiteLinkedInTodas as Vagas

1001 - 5000 funcionários

Fundada em 2011

🤖 Inteligência Artificial

🤝 B2B

Artificial Intelligence • B2B • Consulting

A Tiger Analytics é uma consultoria líder em IA e analytics, especializada em aplicar ciência de dados e Machine Learning para gerar insights estratégicos de negócio em diversos setores. Oferecemos serviços de estratégia de dados, engenharia de IA e business intelligence, viabilizando a tomada de decisão orientada por dados e a transformação digital de nossos clientes. A Tiger Analytics colabora com parceiros de tecnologia de ponta, como Microsoft, Google Cloud e AWS, para entregar soluções de última geração. Atendemos a uma ampla gama de segmentos, incluindo bens de consumo embalados (CPG), saúde e serviços financeiros, ajudando as empresas a operacionalizar insights e a se diferenciar com tecnologias de IA e Machine Learning.

Descrição

• 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

🎯 Requisitos

• 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. • 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.

🏖️ Benefícios

• 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.