Senior ML Engineer, GenAI, AWS

🕒 May 25

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Provectus

WebsiteLinkedInAll Job Openings

501 - 1000 employees

Founded 2012

🤖 Artificial Intelligence

☁️ SaaS

Artificial Intelligence • SaaS

Provectus is an artificial intelligence consultancy and solutions provider that helps businesses transform through AI. Offering both a use case and a platform approach, Provectus integrates AI into organizations to achieve unique business objectives and technical capabilities. Their solutions are cloud-native, vendor-agnostic, and open, allowing for deployment in customer's cloud without restrictive licenses. With applications in industries like retail, manufacturing, and healthcare, Provectus delivers AI-powered use cases and turnkey solutions to drive innovation and efficiency. They also offer consulting, customization, and managed AI services.

📋 Description

• 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.

🎯 Requirements

• 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.

🏖️ Benefits

• 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