Senior ML Engineer (GenAI)

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.