Senior Machine Learning Engineer

Company Description

When you’re one of us, you get to run with the best. For decades, we’ve been helping marketers from the world’s top brands personalize experiences for millions of people with our cutting-edge technology, solutions and services. Epsilon’s best-in-class identity gives brands a clear, privacy-safe view of their customers, which they can use across our suite of digital media, messaging and loyalty solutions. We process 400+ billion consumer actions each day and hold many patents of proprietary technology, including real-time modeling languages and consumer privacy advancements. Thanks to the work of every employee, Epsilon India is now Great Place to Work-Certified™. Epsilon has also been consistently recognized as industry-leading by Forrester, Adweek and the MRC. Positioned at the core of Publicis Groupe, Epsilon is a global company with more than 8,000 employees around the world. For more information, visit epsilon.com/apac or our LinkedIn page.

Job Description

PeopleCloud Customer is a world-class cloud-based Customer Data Platform (CDP) fully enabled with a complete Marketing Automated Operating System (MAOS). The PeopleCloud Customer platform provides out-of-the-box SaaS and PaaS products that are fully integrated, including Customer Identity services, Deterministic and Probabilistic Customer Record stitching services and Marketing Machine Learning algorithms and models trained to deliver personalized activation at scale.

Epsilon India team is looking for a talented team player in a Senior Machine Learning Engineer. You will be part of a team deploying state-of-the-art AI solutions for Epsilon’s enterprise clients. For example, suppose Epsilon’s data scientists create an innovative solution for automatically reading and processing thousands of documents for one of the world’s largest banks. The solution works brilliantly in a development environment, but how should it be deployed into production? How will end users access the solution? How will it scale to processing millions of documents? What tools or platforms should the client use for monitoring? An ML engineer at Epsilon needs to answer these questions AND build out the solution.

Of course we don’t expect you to know everything on day 1! You will report to the Director of Data Science who will provide you with coaching and guidance as you get up to speed. Most importantly you will need to demonstrate the ability to write solid, production-quality code and enthusiasm for becoming an expert in this exciting new career. You will work with a distributed team (onshore and offshore) and work closely with a broadly talented team of delivery management, business analysts, visual designers, analytics, developers, and QA.  You will work directly with clients to own data science solutions as a member of the Data Sciences MML Cloud Services team, and will operate as part of the product team to extend the Platform functionality when not supporting client projects.

RESPONSIBILITIES

• 6+ years of experience in Machine Learning / DevOps / MLOps / Data Engineering
• Design the data pipelines and engineering infrastructure to support our clients’ enterprise machine learning systems at scale
• Take offline models data scientists build and turn them into a real machine learning production system
• Develop and deploy scalable tools and services for our clients to handle machine learning training and inference
• Identify and evaluate new technologies to improve performance, maintainability, and reliability of our clients’ machine learning systems
• Apply software engineering rigor and best practices to machine learning, including CI/CD, automation, etc.
• Support model development, with an emphasis on auditability, versioning, and data security
• Facilitate the development and deployment of proof-of-concept machine learning systems
• Communicate with clients to build requirements and track progress
• Excellent communication & interpersonal skills with an ability to communicate ideas.

Qualifications

• Experience building end-to-end systems as a Platform Engineer, ML DevOps Engineer, or Data Engineer (or equivalent)
• Strong software engineering skills in complex, multi-language systems
• Fluency in Python
• Comfort with Linux administration
• Experience working with cloud computing and database systems
• Experience building custom integrations between cloud-based systems using APIs
• Experience developing and maintaining ML systems built with open source tools
• Experience developing with containers and Kubernetes in cloud computing environments
• Familiarity with one or more data-oriented workflow orchestration frameworks (KubeFlow, Airflow, Argo, etc.)
• Ability to translate business needs to technical requirements
• Strong understanding of software testing, benchmarking, and continuous integration
• Exposure to machine learning methodology and best practices
• Exposure to deep learning approaches and modeling frameworks (PyTorch, Tensorflow, Keras, etc.)