We are looking for a highly skilled and experienced Senior MLOps Engineer to join our team.
This role involves designing, implementing, and maintaining robust and scalable machine learning pipelines.
The ideal candidate will have a strong background in DevOps practices, machine learning principles, and cloud computing platforms.
You will collaborate closely with data scientists and software engineers to streamline deployment and monitoring of ML models, ensuring efficiency and reliability in ML operations.
We hire based on personality, potential, and enthusiasm to make a difference, then provide the tools and skills needed to follow your own path.
You'll gain exposure to a wide range of tools and technologies, with opportunities to become certified in various Cloud and related technologies to develop your own toolkit.
Requirements Software Engineering: Proficiency in programming languages used in ML, such as Python or Java.
Knowledge of software development best practices and methodologies.
Experience with version control systems (e.g., Git).
Familiarity with CI/CD tools and practices.
Strong problem-solving and analytical skills.
Understanding of data structures and algorithms.
Ability to design and develop scalable, efficient, and maintainable software systems.
Experience with microservice architecture and API development.
Machine Learning (ML): Deep understanding of machine learning principles, algorithms, and techniques.
Experience with ML frameworks and libraries like TensorFlow, PyTorch, scikit-learn, or Apache Spark.
Proficiency in data preprocessing, feature engineering, and model evaluation.
Knowledge of ML model deployment and serving strategies, including containerization and microservices.
Familiarity with ML lifecycle management, including versioning, tracking, and model monitoring.
Ability to optimize and fine-tune ML models for performance and accuracy.
Understanding of statistical analysis and experimental design.
Proficiency in data visualization and interpretation of ML results.
Job Responsibilities Proven experience as an MLOps Engineer or similar role, with a strong understanding of AI/ML lifecycle management.
Experience deploying and productionizing ML models.
Familiarity with data engineering concepts, including data pipelines, ETL processes, and big data technologies.
Excellent problem-solving skills and ability to troubleshoot complex AI/ML system issues.
Technical Insights Skills with MLOps concepts and principles.
Experience with cloud platforms (AWS, Google Cloud, Azure) and containerization tools (Docker, Kubernetes).
Proficiency in Python, experience with AI/ML frameworks (TensorFlow, PyTorch), and MLOps tools (Sagemaker, Azure ML, VertexAI, Kubeflow, MLFlow, Seldon, EvidentlyAI).
What We Offer Culture of caring.
At GlobalLogic, we prioritize a culture of caring.
We put people first across all regions and departments, fostering an inclusive environment of acceptance and belonging.
You'll build meaningful connections with teammates, managers, and leaders.
Learning and development.
We are committed to your continuous growth.
You'll have opportunities to learn new skills, advance your career, and participate in programs and training curricula designed for personal and professional development.
Interesting & meaningful work.
Work on impactful projects that matter, helping clients reimagine possibilities and bring innovative solutions to market.
Engage your curiosity and problem-solving skills on cutting-edge projects.
Balance and flexibility.
We support work-life balance through various roles and arrangements, helping you integrate work and life effectively and enjoyably.
High-trust organization.
We value integrity, trust, and transparency.
Join a reliable, ethical global company where honesty and candor are fundamental.
About GlobalLogic GlobalLogic, a Hitachi Group Company, is a trusted digital engineering partner for leading companies worldwide.
Since 2000, we have been driving digital innovation, creating impactful products and experiences, and transforming businesses through intelligent solutions.
#J-18808-Ljbffr