Software Engineer Machine Learning & Java

Job Title: Software Engineer Machine Learning & Java

Location: Sunnyvale, CA

Employment Type: C2C/W2

Job Summary:

We are seeking a highly skilled Software Engineer with expertise in Machine Learning (ML), Natural Language Processing (NLP), Java, Spark, SQL, and Python. The ideal candidate will design, develop, and deploy scalable ML models, working with large datasets and real-time processing frameworks to build intelligent applications.

Key Responsibilities:

• Develop, optimize, and deploy Machine Learning models for real-world applications.
• Design and implement data pipelines using Spark and other big data technologies.
• Work with NLP techniques for text processing, sentiment analysis, and chatbot development.
• Implement Java-based backend solutions to integrate ML models into production systems.
• Write and optimize SQL queries for data extraction, transformation, and analysis.
• Use Python for ML model development, data preprocessing, and automation.
• Collaborate with data scientists, engineers, and product teams to deliver scalable AI-driven solutions.
• Optimize model performance and ensure efficient deployment in cloud or on-premise environments.
• Research and implement the latest advancements in ML and AI.

Required Skills & Qualifications:

• 8 years of experience in software development with a focus on Machine Learning & Java.
• Hands-on experience with NLP, deep learning frameworks (TensorFlow/PyTorch), and Spark.
• Strong knowledge of SQL, database management, and distributed computing.
• Proficiency in Python and Java for building and deploying ML models.
• Experience in designing scalable data processing systems.
• Familiarity with cloud platforms (AWS, Google Cloud Platform, or Azure) is a plus.
• Strong problem-solving and analytical skills.
• Excellent communication and teamwork abilities.

Preferred Qualifications:

• Experience in real-time ML model deployment.
• Knowledge of Kubernetes, Docker, or other containerization tools.
• Understanding of MLOps best practices for continuous deployment of ML models.

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