Machine Learning Engineer

Responsibilities:

• Collect, preprocess, and ensure the quality and integrity of large datasets for machine learning tasks.
• Develop scalable and efficient machine learning models from data science prototypes using various algorithms and frameworks to production.
• Build and maintain machine learning infrastructure, including pipeline orchestration tool, training pipelines, metadata stores, model repositories, feature stores, serving systems, monitoring/feedback loops, and data-drift monitoring and serving an environment for a data science experiment.
• Implement MLOps best practices with automated CI/CD/CT pipelines to streamline development and deployment.

Qualifications:

• 3+ years in Machine Learning Engineer, Data Engineer, Software Engineer or related role, with hands-on experience in Agile environments.
• Proficiency in Python and SQL, with additional experience in Go, being a plus.
• Strong foundation in software engineering best practices (e.g., TDD, SOLID, CLEAN).
• Solid understanding of data science and machine learning fundamentals.
• Experience in working with AWS or other cloud platforms.
• Hands-on experience with deploying and managing highly available systems, including CI/CD pipelines, logging, monitoring, and comfortable with DevOps tools.
• Familiarity with Linux, Docker, Kubernetes, and other tools for building reliable systems.
• Strong problem-solving and critical thinking abilities.
• Excellent communication and teamwork skills.

Nice to have:

• Experience working with infrastructure as code using tools like Terraform
• Experience working with data pipelines, data warehouses, and/or data lakehouse, knowledge of Spark / query optimization is a plus
• Experience in deploying machine learning models into production, both as API services and batches mode.
• Knowledge of NLP, LLMs, vector database, search, and recommendation system