Job Title : Principal Engineer AI/ML & Data Science

Location : Bangalore, India

Experience : 6+ Years

Employment Type : Full-time

Looking for Immediate Joiners

About the Role :

We are seeking a Principal Engineer AI/ML & Data Science to lead the design and development of intelligent systems and data-driven solutions. The ideal candidate will have strong expertise in machine learning, large scale data processing, and AI-driven product development.

You will work closely with engineering, product, and data teams to build scalable machine learning models, AI-powered services, and advanced analytics solutions deployed on modern cloud platforms.

Key Responsibilities :

- Design, develop, and deploy machine learning models and AI-powered applications.

- Build scalable data pipelines and ML workflows for large-scale data processing.

- Develop and optimize predictive models, recommendation systems, and NLP solutions.

- Implement model training, evaluation, and deployment pipelines.

- Work with engineering teams to integrate ML models into production systems.

- Design scalable data architectures and feature engineering pipelines.

- Deploy AI/ML services using cloud infrastructure (AWS preferred).

- Ensure models are scalable, reliable, and production-ready.

- Mentor engineers and contribute to technical strategy and AI roadmap.

Required Skills & Qualifications :

- 6+ years of experience in AI/ML, Data Science, or Applied Machine Learning.

- Strong programming skills in Python (preferred).

- Experience with ML frameworks such as TensorFlow, PyTorch, or Scikit-learn.

- Strong understanding of machine learning algorithms, statistical modeling, and data analysis.

- Experience building production-grade ML systems.

- Hands-on experience with large datasets and distributed data processing.

- Experience with cloud platforms (AWS preferred).

- Strong knowledge of data engineering concepts and ETL pipelines.

- Experience with SQL and NoSQL databases.

Nice to Have :

- Experience with LLMs, Generative AI, or NLP systems.

- Knowledge of MLOps, model monitoring, and model lifecycle management.

- Experience with big data tools such as Spark, Hadoop, or Kafka.

- Experience building recommendation systems or personalization engines.

- Familiarity with Docker, Kubernetes, and CI/CD pipelines.