Senior ML Engineer (GenAI, AWS)

About Provectus

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 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

Data and Programming

• 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

MLOps and Production

• 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

Cloud and Infrastructure

• 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

Will be a plus:

• Practical experience with cloud platforms (AWS stack is preferred, e.g. Amazon SageMaker, ECR, EMR, S3, AWS Lambda)
• Practical experience with deep learning models
• Experience with taxonomies or ontologies
• Practical experience with machine learning pipelines to orchestrate complicated workflows
• Practical experience with Spark/Dask, Great Expectations

What We Offer:

• 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

Interview stages:

• Recruitment Interview
• Tech interview
• HR Interview
• HM Interview