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Work at the forefront of automotive innovation with ZF,
one of the world’s leading automotive suppliers.

Tech Lead - Machine Learning Ops


Hyderabad, TG, IN, 500032

Req ID [[25665]] [[Hyderabad]], India


At ZF Tech Center, India : 


  • We curiously investigate everything and provide opportunity to solve problems analytically, creatively and collaboratively 

  • We believe in learn by doing and provide a space for an entrepreneurial mindset that’s driven by hands-on experimentation 

  • We embrace resilience by seeing every challenge as a learning opportunity and invitation to grow 

  • We are driven by passion for product excellence for building great products with distinct customer value and apply continuous improvement and innovation 


Your Responsibilities as ML Ops Engineer : 


  • Design the data pipelines and engineering infrastructure to support machine learning systems at scale 

  • Take offline models data scientists build and turn them into a real machine learning production system 

  • Define, deploy and manage processes and tools for continuous integration (CI/CD), test-driven development, and release management for ML/DL models (Machine Learning and Deep Learning-based) and data pipelines 

  • Ensure reliability and cost-saving. Scale the proof of concept product to enterprise-grade application with all the required components for reliability, scalability, monitoring and security 

  • Identify and evaluate new technologies to improve performance, maintainability, and reliability of our 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 

  • Work with team members and stakeholders to creatively identify, design, and implement solutions that reduce operational burden, increase reliability and resiliency, ensure disaster recovery and business continuity, enable CI/CD, optimize ML and AI services, etc. 


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 

  • Develop continuous integration and deployment (CI/CD) pipelines on top of Azure that includes AzureML and Azure DevOps 

  • Experimental management and logging frameworks (e.g. MLFlow), data and pipeline versioning. 

  • Expert level fluency in Python 

  • Model & Data Versioning Automated Version Control & tracking of model versions, along with the data used to train it, and some meta-information like training hyperparameters 

  • Exposure to deep learning approaches and modeling frameworks (PyTorch, TensorFlow, Keras, etc.) 

  • Comfort with Linux administration 

  • Experience working with cloud computing (Azure and Hybrid cloud systems) 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 (Dagster, Airflow, 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 


Education & Experience : 

  • 3–5 years' experience building production-quality software. 

  • Bachelors or Master's degree in Computer Science (Compulsory) 


Be part of our ZF team as [[Embedded Software - Machine Learning Engineer]] and apply now!


[[Sriram Adluri]]

Our Commitment to Diversity

ZF is an Equal Opportunity and Affirmative Action Employer and is committed to ensuring equal employment opportunities for all job applicants and employees. Employment decisions are based upon job-related reasons regardless of an applicant's race, color, religion, sex, sexual orientation, gender identity, age, national origin, disability, marital status, genetic information, protected veteran status, or any other status protected by law.

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