Description:

As an MLOps Engineer at Centric Software, you will be responsible for bridging the gap between data science and operations, ensuring seamless integration and efficient deployment of machine learning solutions.
You will work closely with data scientists, software engineers, and DevOps teams to streamline the ML lifecycle and automate processes for model training, testing, deployment, and monitoring.
Your responsibilities will include architecting scalable and reliable infrastructure for model training, testing, and deployment in collaboration with DevOps teams.
You will work closely with data science teams to understand model requirements, assess feasibility, and optimize models for deployment.
You will operate models in production by establishing monitoring and alerting systems to track model performance, detect anomalies, and ensure reliability.
You will optimize resource allocation and utilization for model training and inference to maximize efficiency and cost-effectiveness.
You will implement security best practices and ensure compliance with relevant regulations.
You will stay updated with the latest advancements in ML technologies to drive innovation and best practices within the team.

Requirements:

You must have completed your bachelor's degree or have equivalent experience in computer science, engineering, mathematics, or a similar field.
You should have 5+ years of hands-on experience in data science, DevOps, or data engineering roles.
Proficiency in ML frameworks such as TensorFlow, MLFlow, or scikit-learn is required.
Familiarity with infrastructure-as-code concepts and configuration management to evolve the existing infrastructure is necessary.
You must have expertise in monitoring and logging tools such as Datadog, Prometheus, etc.
A security-minded approach to infrastructure and model deployment is essential.
Excellent problem-solving skills and attention to detail are required, along with persuasive communication and collaboration abilities.
Fluency in English is mandatory.