Senior ML Engineer

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November 14

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Provectus

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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.

501 - 1000 employees

Founded 2012

🤖 Artificial Intelligence

☁️ SaaS

📋 Description

• 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 • 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 • 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.) • Cloud Architecture: Understanding of cloud-native ML architectures • Infrastructure as Code: Experience with Terraform, CloudFormation, or similar