Senior ML Engineer (GenAI)
New
Medellín, Antioquia / Bogotá, Capital District / Cali, Valle del Cauca / Barranquilla / Bucaramanga, SantanderFull-TimeSenior
Salary not disclosed
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Job Details
- Required Skills
- AWSDockerPythonSQLGCPMachine LearningMLFlowNumpyPyTorchPandasSparkTensorflowCI/CDTerraformAWS LambdaCloudFormationPrompt EngineeringLLMGenerative AI
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
- Deep Learning: CNNs, RNNs, Transformers
- 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
- 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
- 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
- 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
Responsibilities
- 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
- Mentor junior and mid-level ML engineers
- Collaborate with cross-functional teams (DevOps, Data Engineering, SAs)
- Stay current with ML research and emerging technologies
- Participate in technical discussions and architectural decisions
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