MLOps Engineer - Data Modeling (6-8 yrs)

Role : Machine Learning Engineer.

Why WWT?

At World Wide Technology, we work together to make a new world happen.

Our important work benefits our clients and partners as much as it does our people and communities across the globe.

WWT is dedicated to achieving its mission of creating a profitable growth company that is also a Great Place to Work for All.

We achieve this through our world-class culture, generous benefits and by delivering cutting-edge technology solutions for our clients.

WWT was founded in 1990 in St.Louis, Missouri.

We employ more than 10,000 people globally and closed nearly $20 billion in revenue in 2023.

We have an inclusive culture and believe our core values are the key to company and employee success.

WWT is proud to have been included on the FORTUNE "100 Best Places to Work For- " list 12 years in a row!.

Want to work with highly motivated individuals on high-performance teams? Join WWT today!.

What is the Solutions Consulting & Engineering (SC&E) Team and why join?.

Solutions Consulting & Engineering is an organization that is Customer Focused and Solutions Led.

We deliver end-to-end (E2E) and emerging solutions to drive customer satisfaction, increase profitability and growth.

Our success is enabled by our world-class management consulting, delivery excellence and engineering brilliance.

We embody the OneWWT mindset by bringing the right talent at the right time from anywhere within WWT to solve our customer's problems.

Our goal is to bring together business acumen with full-stack technical know-how to develop innovative solutions for our clients' most complex challenges.

RESPONSIBILITIES :

- Develop, productionize, and deploy scalable, resilient software solutions for operationalizing AI & ML.

- Deploy Machine Learning (ML) models and Large Language Models (LLM) securely and efficiently, both in the cloud and on-premises, using state of the art platforms, tools, and techniques.

- Provide effective model observability, monitoring, and metrics by instrumenting logging, dashboards, alerts, etc.

- In collaboration with Data Engineers, design and build pipelines for extraction, transformation, and loading of data from a variety of data sources for AI & ML models as well as RAG architectures for LLMs.

- Enable Data Scientists to work more efficiently by providing tools for experiment tracking and test automation.

- Ensure scalability of built solutions by developing and running rigorous load tests.

- Facilitate integration of AI & ML capabilities into user experience by building APIs, UIs, etc.

- Stay current on new developments in AI & ML frameworks, tools, techniques, and architectures available for solution development, both private and open source.

- Coach data scientists and data engineers on software development best practices to write scalable, maintainable, well-designed code.

- Agile Project Work.

- Work in cross-functional agile teams of highly skilled software/machine learning engineers, data scientists, DevOps engineers, designers, product managers, technical delivery teams, and others to continuously innovate AI and MLOps solutions.

- Act as a positive champion for broader organization to develop stronger understanding of software design patterns that deliver scalable, maintainable, well-designed analytics solutions.

- Advocate for security and responsibility best practices and tools.

- Acts as an expert on complex technical topics that require cross-functional consultation.

- Perform other duties as required.

QUALIFICATIONS :

- Experience applying continuous integration/continuous delivery best practices, including Version Control, Trunk Based Development, Release Management, and Test-Driven Development.

- Experience with popular MLOps tools (e. Domino Data Labs, Dataiku, mlflow, AzureML, Sagemaker), and frameworks (e.: TensorFlow, Keras, Theano, PyTorch, Caffe, etc.

- Experience with LLM platforms (OpenAI, Bedrock, NVAIE) and frameworks (LangChain, LangFuse, vLLM, etc.

- Experience in programming languages common to data science such as Python, SQL, etc.

- Understanding of LLMs, and supporting concepts (tokenization, guardrails, chunking, Retrieval Augmented Generation, etc.

- Knowledge of ML lifecycle (wrangling data, model selection, model training, modeling validation and deployment at scale) and experience working with data scientists.

- Familiar with at least one major cloud provider (Azure, AWS, GCP), including resource provisioning, connectivity, security, autoscaling, IaC.

- Familiar with cloud data warehousing solutions such as Snowflake, Fabric, etc.

- Experience with Agile and DevOps software development principles/methodologies and working on teams focused on delivering business value.

- Experience influencing and building mindshare convincingly with any audience.

- Confident and experienced in public speaking.

- Ability to communicate complex ideas in a concise way.

- Fluent with popular diagraming and presentation software.

- Demonstrated experience in teaching and/or mentoring professionals.

Exp : 6 - 8 years