Senior / Lead ML(Ops) Software Engineer

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Job Overview

Atlas AI is seeking a Senior / Lead ML(Ops) Software Engineer to guide our machine learning operations initiatives. In this critical role, you will be responsible for bridging the gap between data science and engineering practices to deploy, monitor, and maintain scalable machine learning models. You will work collaboratively with cross-functional teams to enhance our AI capabilities while ensuring efficient workflows and performance optimization of AI systems.

Job Responsibilities

• Design and implement robust ML workflows for model deployment, monitoring, and retraining.
• Collaborate with data scientists to convert prototypes into scalable production systems.
• Develop and maintain infrastructure using CI/CD practices to streamline the ML lifecycle.
• Evaluate and integrate third-party tools and libraries into existing processes to enhance ML capabilities.
• Monitor drift and performance of ML models in production, ensuring timely updates and adjustments.
• Lead best practices in ML Ops, emphasizing automation, efficiency, and reproducibility.

Qualifications

• Master's degree in Computer Science, Data Science, or a related field; Ph.D. is a plus.
• At least 5 years of experience in machine learning, software engineering, or data engineering roles.
• Proficiency in programming languages such as Python, Java, or Scala.
• Experience with ML frameworks like TensorFlow, PyTorch, or similar.
• Strong understanding of cloud platforms (AWS, GCP, Azure) and containerization technologies (Docker, Kubernetes).
• Knowledge of data engineering practices and familiarity with databases (SQL/NoSQL).
• Excellent problem-solving skills and a strong ability to work in collaborative environments.

Technologies & Tools

The Senior / Lead ML(Ops) Software Engineer will work with a variety of technologies and tools, including but not limited to TensorFlow, PyTorch, Kubernetes, Docker, CI/CD pipelines (Jenkins, GitHub Actions), and cloud platforms such as AWS and Azure. Proficiency in data management tools and visualization software is also essential for optimizing model performance and ensuring successful deployment.

Ideal Candidates

The ideal candidates for this position will possess a strong technical foundation while also exhibiting exceptional communication and collaboration skills. They should be proactive problem solvers with a keen attention to detail and a passion for innovation within the machine learning domain. A successful candidate will thrive in a fast-paced, team-oriented environment and demonstrate a commitment to continuous improvement and operational excellence.