ML Engineer

📋 Description

• Create, develop, and implement machine learning models for predictive analytics, classification, NLP, and various data-driven tasks.

• Build data pipelines for ingestion, preprocessing, feature engineering, and model training.

• Use Docker to containerize ML models and applications for scalable and reproducible deployment.

• Deploy and manage ML solutions in cloud environments (AWS/Snowflake).

• Enhance model performance, latency, and resource utilization for real-time or batch inference.

• Monitor and resolve issues with ML models in production to ensure reliability and robustness.

• Collaborate with Product, Engineering, Data, and business stakeholders to clarify project requirements and integrate ML models into production systems.

• Perform thorough model evaluation using appropriate metrics to ensure performance and fairness.

• Determine whether machine learning is the right solution for a specific problem or if alternative rule-based/statistical approaches are more suitable.

⛳️ Requirements

• A minimum of 4 years of experience as a Software Engineer, with at least 3 years in a Machine Learning Engineer role.

• Strong grasp of machine learning methodologies, including supervised and unsupervised learning, NLP, deep learning fundamentals, and model evaluation.

• Proficiency in Python and ML frameworks such as TensorFlow, PyTorch, Scikit-Learn, Pandas, and NumPy.

• Hands-on experience in containerizing ML applications using Docker for scalable deployment.

• Practical experience with at least one cloud provider (AWS, GCP).

• Solid background in handling large datasets and working with SQL/NoSQL databases.

• Ability to break down complex problems into well-defined ML tasks.

• Skilled in determining whether ML is the optimal solution or if a simpler approach (e.g., heuristic rules, statistical methods) would be more effective.

• Expertise in debugging, optimizing, and enhancing models for performance, efficiency, and interpretability.

• Experience in maintaining ML workflows to ensure reproducibility, scalability, and operational efficiency.

• Excellent communication skills, capable of articulating ML concepts to both technical and non-technical audiences.

• Collaborative and product-focused approach within Agile, cross-functional teams.

• Proactive mindset with a strong sense of ownership, able to lead ML tasks from discovery and experimentation to production deployment and support.

• Commitment to continuous learning with awareness of current ML/AI trends, tools, and best practices.

• Proficiency in English at an Upper-Intermediate level or higher.

🏝️ Benefits

• Remote flexibility: Work in a manner and location that suits you best - we trust you to deliver results.

• Fair compensation: Competitive salary along with benefits that matter (medical, learning opportunities).

• Ownership opportunities: If you identify a problem worth solving, take charge. We encourage smart risks over bureaucratic safety.

• AI enhancement: We utilize AI to enhance your capabilities, complementing rather than replacing your skills.

• Learning investment: Access to English classes and professional development opportunities.

• Career progression: Genuine advancement pathways, not just lateral moves.

• Responsive teammates: No ignored messages, no "not my problem" attitudes.

• Supportive culture: When you're facing challenges, people are there to assist. When issues arise, we address them collaboratively.

• Human connections: Regular meetups, tech talks, and meaningful relationships beyond work.