MLOps Engineer

4 weeks ago

Bangalore, India Ivanan Consultancy & Service Inc Full time
About the Role

We are seeking a highly skilled MLOps Engineer to join our Customer Experience AI ML COE team at Ivanan Consultancy & Service Inc. As a key member of our team, you will be responsible for designing and implementing high-quality, scalable data and analytics applications/platforms for large-scale Machine Learning Distributed systems in an agile environment.

Key Responsibilities:
  • Actively seek out and solve tough data and MLOps engineering problems.
  • Develop and implement data architecture, data warehouse, and hot/warm/cold data storage policies in a cost-optimized way to support AI/ML/Data use cases across the company.
  • Build large-scale data/MLOps pipelines with stream processing and batch processing involving high volume and variety of data.
  • Build architecture and data/MLOps pipelines to perform data ingestion, cleansing, transformation to provide data in proper format and timely basis for predictive analytics problems.
  • Design the data pipelines and engineering infrastructure to support 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 to handle machine learning training and inference.
  • Identify and evaluate new technologies to improve performance, maintainability, and reliability of 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 stakeholders to translate business needs to technical requirements.

Requirements

We are looking for a highly skilled MLOps Engineer with the following qualifications:
  • B.E/B.Tech in Computer Science or Electrical Engineering from a top-tier college and >70% marks.
  • 5-10 years of experience designing, implementing large-scale data engineering and data science distributed systems.
  • Experience building end-to-end systems as a Platform Engineer, ML DevOps Engineer, or Data Engineer (or equivalent).
  • Advanced, inside-out knowledge of multiple data store systems in relational and NoSQL databases, messaging queues, preferably a polyglot programmer who can code in at least 2 high-level languages (Java / Ruby / Python / JS / Go / Elixir).
  • Expert and hands-on experience of fault-tolerant data engineering systems (Hadoop/HDFS/Cassandra/MongoDB/Spark etc.) and multi-datacenter/cloud architectures with at-least 1 cloud platform (AWS, Microsoft Azure, GCP) preferably AWS.
  • Experience working with at least one Data and Machine learning platform (AWS, Palantir, Databricks, Snowflake etc) solving big data predictive analytics problems.
  • 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.).
  • Exposure to machine learning methodology and best practices.
  • Exposure to deep learning approaches and modeling frameworks (PyTorch, Tensorflow, Keras, etc.).
  • Experience working as part of a product team, along with engineers and product managers, to define the problem and execute the data engineering and data science solutions.
  • Ability to understand business concerns and formulate them as technical problems that can be solved using data and math/stats/ML.
  • Experience in dealing with large-scale, noisy, and unstructured data. Experience with time series data will be advantageous.
  • Ability to work on a fast-paced environment & Experience with IoT-based systems preferred.
  • Demonstrable proficiency writing clean and concise code in Java, Python, or R.
  • Strong understanding of software testing, benchmarking, and continuous integration.
  • Passion for driving continual improvement initiatives on engineering best practices like coding, testing, or monitoring. Excellent written and verbal communication skills.

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