Senior Ml Engineer

**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
- 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
- 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.