ML Engineer

As an ML Engineer, you will design, build, and manage machine learning systems, focusing on developing, training, and deploying models at scale. This role involves automating the deployment and management of ML models and integrating them into business and analytical solutions. Collaborating with a larger team, you will contribute to the development and optimization of ML components that drive business value.

Your role

• Design, train, and deploy scalable machine learning models.
• Automate model deployment and management within cloud platforms.
• Collaborate with cross-functional teams to integrate ML solutions into business applications.
• Optimize and fine-tune ML models for performance and accuracy.
• Implement MLOps practices to ensure continuous integration and delivery of models.

Offer

• Long-term freelance contract
• Solid market rates depending on seniority
• Access to top-notch projects

Requirements

• ML Project Experience: Proven experience in developing machine learning models across different projects.
• ML Frameworks & Tools: Hands-on experience with ML technologies such as Apache Airflow, scikit-learn (sklearn), MLFlow, TensorFlow, or similar frameworks.
• MLOps Knowledge: Strong understanding of MLOps architecture and best practices for model deployment and monitoring.
• Data Manipulation: Proficiency in data transformation and manipulation using tools like SQL.
• Cloud Expertise: Experience working within cloud environments, especially data platforms like Google Cloud Platform (GCP) or similar.
• Programming Skills: Proficiency in Python for developing ML solutions.
• Software Engineering Practices: Knowledge of software engineering principles including version control, testing, documentation, and code reviews.
• Automation & Deployment Tools: Familiarity with containerization and orchestration tools such as Docker, Kubernetes, OpenShift, and CI/CD pipelines.

Nice to have:

• Distributed Systems: Experience with distributed systems and tools for batch and streaming data, such as S3, Spark, Kafka, or Flink.
• Monitoring & Observability: Familiarity with monitoring and observability tools, particularly the ELK stack (Elasticsearch, Logstash, Kibana).
• Advanced Analytics & NLP: Understanding of advanced analytics, data science techniques, and natural language processing (NLP).
• Data Pipelines: Experience building and managing complex ETL pipelines.
• System Design: Knowledge of system architecture and design principles.
• Additional Programming Languages: Proficiency in statically typed languages like Scala or Java.
• Database Expertise: Strong understanding of databases, including relational (RDBMS), NoSQL, and time-series databases.
• Agile Experience: Comfortable working within Agile or Scrum development environments.
• Open Source Contributions: Active participation in open-source projects is a plus.