Sr. MLOps Engineer

Job Description

IntroductionAt IBM, work is more than a job it s a calling: To build. To design. To code. To consult. To think along with clients and sell. To make markets. To invent. To collaborate. Not just to do something better, but to attempt things you ve never thought possible. Are you ready to lead in this new era of technology and solve some of the world s most challenging problems If so, lets talk.Your Role and Responsibilities We re looking for an experienced, motivated hands-on engineer with a focus on Machine learning engineering and MLOps. As a ML engineer you will be a very impact role applying your deep expertise in designing, developing, delivering and supporting a world class software product. You will be supporting multiple business functions within the product with the various data science initiatives. You will leverage open source and cloud tools to build and develop reusable components and architecture that can enable the data science teams to provide best in class AI/ML environment. You will also help in providing technical direction and develop strategies for long-term growth of the product. You need to be versatile, display leadership qualities and open minded to take on new problems that our customers face. The day today responsibilities include,
• Analyze, design and develop reusable components or modules supporting applications use of AI/ML models
• Apply software engineering best practices to machine learning including AI/MLOps, CI/CD, automation etc
• Partner and work with data scientists in the team in taking data science algorithms and integrating them efficiently for high scale production application.
• Design, develop, troubleshoot, and scale the modeling pipelines required to support the various analytics and AI/ML workloads.
• Understand and designs reusable components of the modeling pipelines required to support the model generation, model serving, model versioning, model auditing
• Design and develop tooling and CI/CD pipelines to research, experiment, train, package, deploy and validate various ML model types and frameworks.
• Design, develop tools and processes, to monitor the ML models in production, monitoring drift and performance
• Translate complex technical and functional problems into detailed designs
• Provide senior level support and mentoring by evaluating product enhancements for feasibility studies and providing completion time estimates
• Develop high quality unit, tests functional tests and integration tests supporting the data and modeling pipelines
• Ensure product quality by participating in design reviews, code reviews and working with the team for end-to-end validation of the entire product
• Define data governance, data auditing policy and strategies for compliance and security controls
• Write and maintain technical documentation for the various projects. Review product user documentation for technical accuracy and completeness
Required Technical and Professional Expertise
• 7-8 years of experience in developing enterprise applications using Java, Python, spark and related technologies with 2+ years developing software s with a focus a focus on AI/ML integration, MLOps
• Software development strategies for low latency, high throughput softwares
• Hands-on experience with common distributed processing tools and languages Python, Spark, Hive,
• 2+ years experience in supporting AI/ML workloads in production with a focus on model serving and model monitoring
• 2+ year experience working with Kubernetes and docker and experience building on top of these native platforms
• Good understanding of ML model lifecycle including various data processing strategies, data engineering, model training, evaluating and serving
• Deep understanding of data and modeling pipelines, schema management
• Experience with specialized data architectures like data lake, data mesh and optimizing data layouts for efficient processing.
• Hands on Experience with streaming platforms and frameworks like Kafka, spark-streaming
• Experience with ML lifecycle platforms and tools like Kubeflow, MLFlow
• Familiarity with pipeline orchestrator tools like Argo workflows, Airflow
• Understanding of DevOps principles and experience building a and designing a DevOps first way of working
• Good written and verbal communication skills
• Ability to provide guidance to less experienced team members.
Preferred Technical and Professional Expertise
• Production Experience with serverless compute platforms
• Experience working with teams with focus on supply chain data
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Keyskills :
open sourcelow latencysupply chaindata sciencedata processingproduct qualitydata governancehigh throughputmachine learningdata engineeringcommercial modelssecurity controlsuser documentationfeasibility studiestechnical directioncommunicatio