Job Location: Bangalore/Bengaluru
Machine Learning Operations Specialist – Big Data/Artificial Intelligence
As a MLOps Engineer, you will help define and implement cloud-based infrastructure for Machine Learning and Artificial Intelligence initiatives. In this critical role, you will define MLOps practices and deliver new capabilities for model training, model packaging, deployment, ongoing monitoring tools, and metrics. As a primary subject matter expert, you will be providing consultation services and guiding various build and operations teams to productionize ML workloads.
The role is part of Cloud Data Engineering team that leverages emerging cloud technologies to build Data Pipelines, and enterprise scale Data Platforms supporting Analytics and MLAI solutions. CDE Team leverages agile DevOps (DataOps, MLOps) practices, and automation to enable speed, quality, and provide a seamless experience to customers.
Responsibilities :
– Design prototypes, build, and refine scalable infrastructure for deploying machine learning workloads at scale
– Define MLOps practices, and deliver new capabilities for model training, model packaging, deployment, ongoing monitoring tools, and metrics.
– Define multi-tenant architecture with containerization leveraging GCP Services for Machine Learning and NLP based projects
– Develop pipelines and automations to manage ML infrastructure, deployments, operations and tracking effectiveness of the models
– Work directly with Data Scientists, ML engineers and other stake holders to enable successful deployments of MLAI workloads
– Use deep subject matter expertise, influence and process skills to help stakeholders identify and meet their high priority needs while considering cultural and diversity implications.
– Encourage informed risk-taking and act as a catalyst for innovation at Cardinal Health
– Proactively develop, maintain and evangelize technical knowledge in Machine Learning and adjacent technologies.
– Establish development and delivery best practices in order for ML implementations to minimize rework and maintenance cost. Build ML architectures that work at Cardinal Health scale.
Desired Qualifications :
– Bachelor’s degree preferred or equivalent work experience
– 5+ years of engineering experience in Big Data systems, Data Analytics and MLAI related field
– Hands-on experience architecting and designing data lakes on GCP serving analytics and MLAI workloads
– Hands-on experience managing Data and ML Platforms at production scale
– Strong background in Cloud Computing and Distributed Software design and development
– Familiarity with Kubernetes or other container orchestration tools in a production setting
– Experience in artificial intelligence, cognitive computing, machine learning and related technologies
– Experience in designing and optimizing ML models using GCP ML technologies
– Experience with AirFlow, KubeFlow and BigQuery is preferred
– Experience integrating GCP or 3rd party KMS, HSM with GCP data services for building secure data solutions
– Experience with CICD pipelines such as Concourse, Jenkins
– Ability to work in a fast-paced environment and strong technical communication skills
– Agile development skills and experience
– Google Cloud Platform certification is a plus
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