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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 ResponsibilitiesTechnical Delivery (60%)Design and implement end-to-end ML solutions from experimentation to productionBuild scalable ML pipelines and infrastructureOptimize model performance, efficiency, and reliabilityWrite clean, maintainable, production-quality codeConduct rigorous experimentation and model evaluationTroubleshoot and resolve complex technical challengesCollaboration and Contribution (25%)Mentor junior and mid-level ML engineersConduct code reviews and provide constructive feedbackShare knowledge through documentation, presentations, and workshopsCollaborate with cross-functional teams (DevOps, Data Engineering, SAs)Contribute to internal ML practice developmentInnovation and Growth (15%)Stay current with ML research and emerging technologiesPropose improvements to existing solutions and processesContribute to the development of reusable ML acceleratorsParticipate in technical discussions and architectural decisionsRequirementsMachine Learning CoreML Fundamentals: supervised, unsupervised, and reinforcement learningModel Development: feature engineering, model training, evaluation, hyperparameter tuning, and validationML Frameworks: classical ML libraries, TensorFlow, PyTorch, or similar frameworksDeep Learning: CNNs, RNNs, TransformersLLMs and Generative AILLM Applications: Experience building production LLM-based applicationsPrompt Engineering:



Ability to design effective prompts and chain-of-thought strategiesRAG Systems: Experience building retrieval-augmented generation architecturesVector Databases: Familiarity with embedding models and vector searchLLM Evaluation: Experience with evaluation metrics and techniques for LLM outputsData and ProgrammingPython: Advanced proficiency in Python for ML applicationsData Manipulation: Expert with pandas, numpy, and data processing librariesSQL: Ability to work with structured data and databasesData Pipelines: Experience building ETL/ELT pipelines – Big Data: Experience with Spark or similar distributed computing frameworksMLOps and ProductionModel Deployment: Experience deploying ML models to production environmentsContainerization: Proficiency with Docker and container orchestrationCI/CD: Understanding of continuous integration and deployment for MLMonitoring: Experience with model monitoring and observabilityExperiment Tracking: Familiarity with MLflow, Weights and Biases, or similar toolsCloud and InfrastructureAWS Services: Strong experience with AWS ML services (SageMaker, Lambda, etc.)GCP Expertise: Advanced knowledge of GCP ML and data servicesCloud Architecture: Understanding of cloud-native ML architecturesInfrastructure as Code: Experience with Terraform, CloudFormation, or similarWill be a plusPractical 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.#J-*****-Ljbffr

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📌 Senior Ml Engineer (Genai) (Medellín)
🏢 Provectus
📍 Medellín