
(Apply in 3 Minutes) Artificial Intelligence Engineer
4 weeks ago
We're Hiring | Senior Machine Learning Engineer – Applied AI | Bangalore (FANG hiring)
Are you passionate about building scalable, intelligent systems that solve real-world problems?
Do you enjoy transforming cutting-edge machine learning research into production-ready solutions?
We are hiring a Senior Machine Learning Engineer for our Applied AI team based in Bangalore. This is a high-impact role where you’ll work at the intersection of software engineering and data science to bring AI solutions to life.
🔍 What You'll Do
- Lead the design, development, and deployment of machine learning models into scalable, production environments
- Build robust, testable ML pipelines and APIs using strong software design principles
- Collaborate with data scientists to develop and optimize models (supervised, deep learning, RL, etc.)
- Implement MLOps best practices: CI/CD for ML, model versioning, monitoring, and automation
- Design data pipelines for model training, inference, and performance tracking
- Work with cross-functional teams to integrate ML into core products
- Stay current with ML advancements and propose practical innovations
✅ What We're Looking For
- 4-9+ years of hands-on experience in ML engineering or related roles
- Strong Python programming skills; exposure to other languages like Java, C++, or Go is a plus
- Solid understanding of software design patterns, algorithms, and distributed systems
- Experience with ML frameworks like PyTorch or Scikit-learn
- Skilled in feature engineering, model evaluation, and performance tuning
- Familiar with SQL/NoSQL databases and big data handling
- Strong communication skills and the ability to simplify technical topics
🎓 Preferred Qualifications
- Master’s or PhD in Computer Science, Data Science, or a related field
- Experience with tools like Spark, Kafka, or Hadoop
- Exposure to A/B testing and experimentation frameworks
- Contributions to open-source or a strong personal project portfolio
- Understanding of ML model governance, bias mitigation, and privacy standards
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