Engineer ML
Albertsons Companies India
Job Description
Position Title: Engineer ML Job Description: Key Responsibilities: Design, build, evaluate, ship, and refine products by hands-on ML development. Work closely with Enterprise Architects and Technical Owners to create applications aligned with the business strategy. Work closely with Data Science team to build and optimize machine learning pipelines.
Collaborate with a cross functional agile team spanning research, data science, product management, and engineering to build new product features using machine learning to solve problem in supply chain, pricing, fulfilment & personalization. Continuously learn and adapt to an ever-changing technology landscape to bring varied technology options to the table. Participate in design sessions, brainstorming various options, discussing pros and cons, and helping drive consensuses.
Experience Required: 1-3 years of experience in Data Engineering or Machine Learning. Programming knowledge and/or experience in either Python, Scala. Programming knowledge and/or experience of Distributed Computing frameworks such as Apache Spark, Apache Flink, Hadoop, DASK or Ray.
Knowledge of SQL / NoSQL database technologies (Oracle, SQL Server, Cassandra, Cosmo DB, MongoDB). Knowledge of Feature Engineering for Machine Learning, Model Evaluation, Model Deployment. Knowledge of Micro Services (Django, Fast API).
Knowledge of software development methodologies (Agile). Ability to troubleshoot and debug programs. Competencies: Compassionate and kind, showing courtesy, dignity, and respect.
They show sincere interest and empathy for all others. Show integrity in what is done and how it is done - without sacrificing personal/business ethics. Team-oriented, positively contributing to team morale and willing to help.
Learning-Focused, finding ways to improvise in their field and use positive constructive feedback to grow personally and professionally. Mandatory Skills Required: Python, Scala (or Java). Cosmos DB, Mongo DB / Non-SQL and SQL database management.
Apache Spark/Flink/Hadoop/ DASK/ Ray. Azure/GCP, Kubernetes, CI/CD pipeline, GitHub / Version control. RESTful APIs.
Machine Learning libraries such as scikit-learn, SciPy, PyTorch, TensorFlow. Additional Skills Required: Supervised & Unsupervised Machine Learning algorithms. Data streaming platform (e.g.
Kafka). Data warehouse or Delta Lake.