Provectus
Healthcare & Life Sciences, Retail & CPG, Media & Entertainment, Manufacturing, Internet businesses
SeniorMLEngineer(GenAI,AWS)
Neural analysis suggests this role is
optimal for Senior candidates.
“Senior ML Engineer (GenAI, AWS) at Provectus. Skills: ML Fundamentals, Model Development, Deep Learning, LLMs and Generative AI, Python, Data Manipulation, Data Pipelines, Big Data, MLOps and Production, Cloud and Infrastructure, AWS, GCP. Design and implement end-to-end ML solutions from experimentation to production. Build scalable ML pipelines”
Industry & Context.
Troubleshoot and resolve complex technical challenges
What They're Looking For.
Must Have
ML Fundamentals: supervised, unsupervised, and reinforcement, Model Development: feature engineering, model training, evaluation, hyperparameter tuning, ML Frameworks: classical ML libraries, TensorFlow, PyTorch, or similar, Deep Learning: CNNs, RNNs, Transformers, LLM Applications: Experience building production LLM-based, Prompt Engineering: Ability to design effective prompts and chain-of-thought, RAG Systems: Experience building retrieval-augmented generation, Vector Databases: Familiarity with embedding models and vector, LLM Evaluation: Experience with evaluation metrics and techniques for LLM outputs, Python: Advanced proficiency in Python for ML, Data Manipulation: Expert with pandas, numpy, and data processing, SQL: Ability to work with structured data, 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, Containerization: Proficiency with Docker and container, CI/CD: Understanding of continuous integration and deployment for, Monitoring: Experience with model monitoring, Experiment Tracking: Familiarity with MLflow, Weights and Biases, or similar tools, AWS Services: experience with AWS ML services (SageMaker, Lambda, etc.), GCP Expertise: Advanced knowledge of GCP ML and data, Cloud Architecture: Understanding of cloud-native ML, Infrastructure as Code: Experience with Terraform, CloudFormation, or similar
Nice to Have
Practical experience with cloud platforms (AWS stack is preferred, e. g. Amazon SageMaker, ECR, EMR, S3, AWS Lambda), Practical experience with deep learning, Experience with taxonomies, Practical experience with machine learning pipelines to orchestrate complicated, Practical experience with Spark/Dask, Great Expectations
What You'll Do.
Design and implement end-to-end ML solutions from experimentation to production
Build scalable ML pipelines
Optimize model performance
production-quality code
Conduct rigorous experimentation and model evaluation
Troubleshoot and resolve complex technical challenges
How You'll Work.
Team & Collaboration
Collaborate with cross-functional teams (DevOps, Data Engineering, SAs); Collaborate with cross-functional teams
Communication Scope
Share knowledge through documentation, presentations
Full Job Description
## Description Provectus helps companies adopt ML/AI to transform the ways they operate, compete, and drive value. The focus of the company is on building ML Infrastructure to drive end-to-end AI transformations, assisting businesses in adopting the right AI use cases, and scaling their AI initiatives organization-wide in such industries as Healthcare & Life Sciences, Retail & CPG, Media & Entertainment, Manufacturing, and Internet businesses. As an ML Engineer, you’ll be provided with all opportunities for development and growth. Let's work together to build a better future for everyone! ## Responsibilities 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 Machine Learning Core - 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. LLMs and Generative AI - LLM Applic
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