Your responsibilities

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
Collaborate with Data Scientists, Domain Experts and Product Owners to build and deploy the end-to-end AI Products

Our requirements

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 in Python
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
Very good English skills

Understanding of Deep learning platforms such as Keras, Tensor-flow and/or PyTorch
Understanding of key Machine Learning & Deep Learning Algorithms
Knowledge of Oilfield terminology and business practices
Experience with IoT/ Edge technology
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