**Senior ML Engineer (GenAI)** ============================== Colombia, Medellín, Antioquia; Bogotá, Capital District; Cali, Valle del Cauca; Barranquilla; Bucaramanga Metropolitan Area; Bucaramanga, Santander **About project** 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. **Core Responsibilities:** * 1. 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 * 2. 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 * 3. 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:** * 1. 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 * 2. 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 * 3. 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 * 4. 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 * 5. 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.