Demo

Software Engineer – Machine Learning & Java

Tror AI for everyone
Sunnyvale, CA Full Time
POSTED ON 4/24/2025
AVAILABLE BEFORE 5/21/2025

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, GCP, 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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