Senior ML Engineer (GenAI)

About the Role:

As a Senior ML Engineer at Provectus, you'll be responsible for designing, developing, and deploying production-grade machine learning solutions for our clients. You will work on complex ML problems, mentor junior engineers, and contribute to building ML accelerators and best practices.

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.

Originally posted on Himalayas