Machine Learning Ops Engineer

About Sonata Software

In today's market, we observe a distinct duality in technology adoption. On one front, clients are keenly focused on cost containment, while on the other, there is a strong drive to modernize their digital storefronts, aiming to appeal to both consumers and B2B customers alike.

As a leading Modernization Engineering company, we aim to deliver modernization-driven hypergrowth for our clients based on the deep differentiation we have created in Modernization Engineering, powered by our Lightening suite and 16-step Platformation™ playbook. In addition, we bring agility and systems thinking to accelerate time to market for our clients.

Headquartered in Bengaluru, India, Sonata Software has a strong global presence, with strategic operations spanning across key regions such as the US, UK, Europe, APAC, and ANZ. We are a trusted partner of world-leading companies in TMT (Telecom, Media, and Technology), Retail & CPG, Manufacturing, BFSI (Banking, Financial Services and Insurance), and HLS (Healthcare and Lifesciences). Our bouquet of Modernization Engineering services cuts across Cloud, Data, Dynamics, Contact Centers, and around newer technologies like Generative AI, MS Fabric, and other modernization platforms.

To know more, visit: www.sonata-software.com

If this opportunity aligns with your aspirations, please share your updated profile with Udayashree( [email protected] )

Machine Learning Ops Engineer

Experience - 7 to 10 yrs (5 yrs relevant)

Key Responsibilities:

· Develop and maintain MLOps processes that facilitate model development, training, deployment, monitoring, and inference

· Deploy machine learning models into production environments, ensuring scalability and reliability

· Fine-tune machine learning models using various algorithms and techniques

· Supports the operations of the deployed solutions, investigates complex issues and assists with the resolution and implementation of preventive measures.

· Utilize Databricks for analyzing large datasets, identifying patterns, and correlations

· Employ Databricks’ data handling capabilities to clean, normalize, and preprocess data efficiently.

· Address missing data, outliers, and anomalies using Databricks’ robust data processing tools

· Feature Engineering Development in Databricks Environment

· Model Design, Model Build & Model Training

· Training Validation

· Hyper parameter tuning

· Model Deployment & inferencing MLOPS MFLOW Databricks, Rest API

· Design and engineer new features using Databricks’ collaborative notebooks and MLflow for tracking experiments

Minimum Requirements:

Education:

Bachelor’s degree in Computer Science, Statistics, Mathematics, or related field.

Experience:

Minimum of 3 years of experience in data science - centric roles with a strong focus on analytics, data science, and machine learning.

Technical Skills:

· Proficiency in Python, SQL, Spark with strong programming skills. Experience in Databricks platform, including Delta Lake, Databricks SQL, and MLflow.

· Strong understanding of machine learning algorithms, model evaluation, and optimization techniques

· Proven track record of developing and deploying models on Databricks

Analytical Skills:

Robust analytical and problem-solving skills with a solid foundation in statistical methods.

Expertise in translating business issues into data-driven solutions using Databricks.