MLOps Engineer

About Ascendion

Ascendion is a full-service digital engineering solutions company. We make and manage software platforms and products that power growth and deliver captivating experiences to consumers and employees. Our engineering, cloud, data, experience design, and talent solution capabilities accelerate transformation and impact for enterprise clients. Headquartered in New Jersey, our workforce of 7,000+ Ascenders delivers solutions from around the globe. Ascendion is built differently to engineer the next.

Ascendion | Engineering to elevate life | www.ascendion.com

We have a culture built on opportunity, inclusion, and a spirit of partnership. Come, change the world with us:
 Build the coolest tech for the worlds leading brands
 Solve complex problems - and learn new skills
 Experience the power of transforming digital engineering for Fortune 500 clients
 Master your craft with leading training programs and hands-on experience

Experience a community of change-makers!

Join a culture of high-performing innovators with endless ideas and a passion for tech. Our culture is the fabric of our company, and it is what makes us unique and diverse. The way we share ideas, learning, experiences, successes, and joy allows everyone to be their best at Ascendion.

About the Role:

Location: Pune MH (hybrid work)

Description:

Responsibilities:
 Support the rollout, operations, and monitoring of a muti-nodal LLM pipeline.
 Act as a reliable and dependable source of mediation between Data science teams, and Infrastructure teams as it relates to Machine learning solutions within a highly paced environment
 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
 Ability to interact with Model Risk Management groups to get the models certified
 Develop and deploy scalable tools and services for our clients to handle machine learning training and inference
 Identify and evaluate new technologies to improve the 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.
 Manage the Model Management Systems comprising of Build-Train-Select-Integrate-Deploy-Monitor ML models.

Qualifications:
 2-5 years experience building production-quality software.
 Bachelors or Masters degree and/or equivalent professional experience
 Experience building end-to-end systems as a Platform Engineer, ML DevOps Engineer, or Data Engineer (or equivalent) in an LLM-focused Model Management System involving vector databases, RAG, custom ML models
 Strong software engineering skills in complex, multi-language systems in Python and Linux environment
 Fluency in Python core such as operators, lists, dictionaries, NumPy, Math functions, and methods
 Fluency in Data operations and libraries using DataFrames, Pandas, Anaconda
 Good grasp of all the core machine learning concepts, such as supervised, unsupervised, and reinforcement learning
 Exposure to deep learning approaches and modeling frameworks (PyTorch, Flask, Tensorflow, Keras, etc.)
 Comfort with Linux administration and scripting
 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

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