Senior ML Engineer, GenAI, AWS

🕒 Maio 25

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Provectus

SiteLinkedInTodas as Vagas

501 - 1000 funcionários

Fundada em 2012

💼 Consultoria

🏥 Saúde

🏭 Manufatura

Consulting • Healthcare • Manufacturing

Provectus é uma consultoria e provedora de soluções em inteligência artificial que ajuda empresas a se transformarem por meio de IA. Oferecendo tanto uma abordagem orientada a casos de uso quanto uma abordagem de plataforma, a Provectus integra IA às organizações para alcançar objetivos de negócio específicos e ampliar capacidades técnicas. Suas soluções são nativas em nuvem, agnósticas a fornecedores e abertas, permitindo a implantação na nuvem do cliente, sem licenças restritivas. Com aplicações em setores como varejo, manufatura e saúde, a Provectus entrega casos de uso impulsionados por IA e soluções prontas para uso para acelerar a inovação e a eficiência. A empresa também oferece consultoria, customização e serviços gerenciados de IA.

Descrição

• Technical Delivery (60%) • - Design and implement end-to-end ML solutions from experimentation to production; • - Build scalable ML pipelines and infrastructure; • - Optimize model performance, efficiency, and reliability; • - Write clean, maintainable, production-quality code; • - Conduct rigorous experimentation and model evaluation; • - Troubleshoot and resolve complex technical challenges. • Collaboration and Contribution (25%); • - Mentor junior and mid-level ML engineers; • - Conduct code reviews and provide constructive feedback; • - Share knowledge through documentation, presentations, and workshops; • - Collaborate with cross-functional teams (DevOps, Data Engineering, SAs); • - Contribute to internal ML practice development. • Innovation and Growth (15%) • - Stay current with ML research and emerging technologies; • - Propose improvements to existing solutions and processes; • - Contribute to the development of reusable ML accelerators; • - Participate in technical discussions and architectural decisions.

🎯 Requisitos

• ML Fundamentals: supervised, unsupervised, and reinforcement learning; • Model Development: feature engineering, model training, evaluation, hyperparameter tuning, and validation; • ML Frameworks: classical ML libraries, TensorFlow, PyTorch, or similar frameworks; • Deep Learning: CNNs, RNNs, Transformers. • LLM Applications: Experience building production LLM-based applications; • Prompt Engineering: Ability to design effective prompts and chain-of-thought strategies; • RAG Systems: Experience building retrieval-augmented generation architectures; • Vector Databases: Familiarity with embedding models and vector search; • LLM Evaluation: Experience with evaluation metrics and techniques for LLM outputs. • Python: Advanced proficiency in Python for ML applications; • Data Manipulation: Expert with pandas, numpy, and data processing libraries; • SQL: Ability to work with structured data and databases; • Data Pipelines: Experience building ETL/ELT pipelines - Big Data: Experience with Spark or similar distributed computing frameworks. • Model Deployment: Experience deploying ML models to production environments; • Containerization: Proficiency with Docker and container orchestration; • CI/CD: Understanding of continuous integration and deployment for ML; • Monitoring: Experience with model monitoring and observability; • Experiment Tracking: Familiarity with MLflow, Weights and Biases, or similar tools. • AWS Services: Strong experience with AWS ML services (SageMaker, Lambda, etc.); • GCP Expertise: Advanced knowledge of GCP ML and data services; • Cloud Architecture: Understanding of cloud-native ML architectures; • Infrastructure as Code: Experience with Terraform, CloudFormation, or similar.

🏖️ Benefícios

• Long-term B2B collaboration • Fully remote setup • A budget for your medical insurance • Paid sick leave, vacation, public holidays • Continuous learning support, including unlimited AWS certification sponsorship