Recro | MLOPS ENGINEER | bangalore

2 months ago

bangalore, India Recro Full time

Key skills: Python Programming, MLOps Frameworks Proficiency, Machine Learning Systems Design

Currently, we are looking for ML Ops Engineer. In this role, you will have the opportunity to design the data pipelines and engineering infrastructure to support our clients’ enterprise machine learning systems at scale and 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

Does that sound a bit like you? Let’s talk Even if you don’t tick all the boxes below, we’d love to hear from you; our new department is rapidly growing and we’re looking for many people with the can-do mindset to join us on our digitalization journey. Thank you for considering DHL as the next step in your career – we do believe we can make a difference together

What will you need?

  • University Degree in Computer Science, Information Technology, or related field
  • 6+ years of experience in the Machine Learning Operations role
  • 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
  • Strong analytic skills related to working with structured, semi structured and unstructured datasets
  • Docker and Kubernetes
  • Experience in using popular MLOps frameworks like Kubeflow, MLFlow, and DataRobot
  • Ability to build MLOps pipelines
  • Strong Python programming knowledge
  • Kubeflow
  • Poetry for package management
  • Black, ruff, isort, flake8 for code cleanup
  • Github Actions
  • Machine learning techniques: Decision Trees, Random Forest, Boosting Algorithm, Neural Networks, Deep Learning, Support Vector Machines, Clustering, Bayesian Networks, Reinforcement Learning, Feature Reduction / engineering, Anomaly deduction, Natural Language Processing (incl. sentiment analysis, Topic Modeling), Natural Language Generation
  • Ability to design and implement cloud solutions - Azure
  • Awareness of Agile / Scrum ways of working
  • Identify the right modeling approach(es) for given scenario and articulate why the approach fits
  • Assess data availability and modeling feasibility
  • Review interpretation of models results
  • Experience in Logistic industry domain would be added advantage

You should have:

  • Certifications in some of the core technologies
  • Ability to collaborate across different teams/geographies/stakeholders/levels of seniority
  • Customer focus with an eye on continuous improvement;
  • Energetic, enthusiastic and results-oriented personality
  • Ability to coach other team members, you must be a team player
  • Strong will to overcome the complexities involved in developing and supporting data pipelines


Ready to embark on the journey? Here’s what we are looking for:

As a Senior ML Ops Engineer, having Machine Learning skill set is a huge plus. Very good knowledge of ML Model deployment will also be an integral part of this role.

You are a technology aficionado, therefore you have a good understanding of Python, CI/CD pipeline. You are able to work independently, prioritize and organize your tasks under time and workload pressure. Working in a multinational environment, you can expect cross-region collaboration with teams around the globe, thus being advanced in spoken and written English will be certainly useful.

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