Provectus
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.
Senior ML Engineer
Job not on LinkedIn
November 14
Provectus
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.
đ 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
đŻ 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
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