ML Ops Engineer

Job Description

Machine Learning Engineer (MLE) role, will be responsible for guiding the successful development, automation and governance of AI/ML Solutions to the internal customers, collaborating with Data Scientists, Domain Experts and Product Owners to build and deploy the end-to-end AI Products for the company.

The role will require working knowledge of Cloud, DevOps/ MLOps, Machine Learning, Deep Learning and Application Development. This position will also be involved in the formulation of key business requirements to be solved and rationalizing the various best practices to solve those problems.

YOUR ROLE

•Script production-ready codes
•Leveraging GPU & CPU resources as appropriate / understanding capacity requirements for ML Workloads
•Architect, automate and orchestrate Dev-Ops/ ML-Ops pipelines on cloud
•Craft re-usable tools and frameworks to monitor, optimize and maintain ML solutions
•Design and implement data engineering pipelines (ETL)
•Understand business problems and collaborate within the team to ensure scalability, business continuity and appropriate turnaround time
•Conduct internal workshops and external meetups, participate in external conferences, and give talks
•Continuously evolve your craft by keeping up to date with the new developments in AI/ML and related technologies and upskilling on these as needed.

YOUR PROFILE

•A bachelor's or master’s degree in Computer Science, Operations Research, Mathematics and Computing with 3-4 years of relevant experience
•Experience of working with large data sets, coming from varied sources
•Excellent Programming Skills (Python Preferred)
•Experience with containerization (Docker) and container orchestration using Kubernetes
•Experience in Cloud Application Development (Google Cloud Platform & Azure Preferred)
•Great at solving problems; debugging; troubleshooting; and designing & implementing solutions to complex technical issues
•Experience designing, building, and maintaining ETL workflows and data pipelines, using orchestration tools like Apache Airflow, Kubeflow, or similar
•Proficiency with DevOps practices, including CI/CD, Git-based workflows
•Comfortable working in Unix/Linux environments
•Hands on experience of designing, building, and supporting RESTful APIs
•Working knowledge of machine learning and deep learning concepts, and experience supporting model deployment and monitoring

Nice to have:

•Understanding of Deep learning platforms such as Keras, Tensor-flow and/or PyTorch is highly desirable
•In-Depth Understanding of key Machine Learning & Deep Learning Algorithms
•Knowledge of Oilfield terminology and business practices
•Experience with IoT/ Edge technology is a plus
•Recognized open-source contributions
•Familiarity with data engineering tools (Flink/Spark/Kafka etc.)
•Experience with big data stack (Spark, Hadoop, Storm, Hive & Pig) and NoSQL stores is a plus

WHAT YOU'LL LOVE ABOUT WORKING HERE:

•Flexible and dynamic work environment involving teams spread across different global locations;
•Communities with shared interests and identities, all working together to advance diversity, equity and inclusion within the workplace and society;
•Do you want to learn more? You will have access to various training programs and platforms (e.g.: content provided by Pluralsight, Harvard and Coursera);
•Attractive benefits package (meal tickets, medical services, Benefit online, Employee Share Option Plan).

Capgemini is an equal opportunity employer. We promote equality and dignity in all aspects of recruitment and employment, and all offers of employment and promotions are made on the basis of performance, competence and ability.