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.