Enphase Energy is a global energy technology company and leading provider of solar, battery, and electric vehicle charging products. Founded in 2006, Enphase transformed the solar industry with our revolutionary microinverter technology, which turns sunlight into a safe, reliable, resilient, and scalable source of energy to power our lives. Today, the Enphase Energy System helps people make, use, save, and sell their own power. Enphase is also one of the fastest growing and innovative clean energy companies in the world, with approximately 68 million products installed across more than 145 countries.
We are building teams that are designing, developing, and manufacturing next-generation energy technologies and our work environment is fast-paced, fun and full of exciting new projects.
If you are passionate about advancing a more sustainable future, this is the perfect time to join Enphase!
About the role
For our Customer Experience AI ML COE team, we seek Hands-On MLOps Engineer who can work on designing & implementing high quality scalable data and analytics applications/platforms for large scale Machine Learning Distributed systems in agile environment. You will be part of a team deploying state-of-the-art AI ML solutions for Enphase customers.
Your ability to lead the data architecture, design, and implementation of maintainable, high-quality, and high-performing data analytics systems and AI applications is essential for success in this role. Understanding of data modelling, transformation in big data/ML environment and prior experience of building Data/MLOps pipelines using industry proven Data/ML platforms is must for this role.
What you will be doing
Actively seek out and solve tough data and MLOps engineering problems. Develop and Implement Data Arch, Data Warehouse and Hot/Warm/Cold data storage policy in 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
Who you are and what you bring:
B.E/B.Tech in Computer Science or Electrical Engineering from top tier college and >70% marks. 5-10 years’ 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 system 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