About Project 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. Asan ML Engineer, you’ll beprovided 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); 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 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; LLMs and Generative AILLM 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 ProgrammingPython: 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 ProductionModel 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 InfrastructureAWS 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 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; HR Interview; HM Interview. We are waiting for you to become a part of our team! #J-18808-Ljbffr