Senior ML Engineer
hace 1 semana
- 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
- 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.) - Cloud Architecture: Understanding of cloud-native ML architectures - Infrastructure as Code: Experience with Terraform, CloudFormation, or similar
- 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.
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