About the Role :

We are seeking a skilled Data / ML Engineer to design, develop, and deploy scalable machine learning pipelines and data engineering solutions that power AI-driven applications. The ideal candidate should have strong expertise in data processing, machine learning frameworks, and MLOps, with hands-on experience building production-grade ML systems and integrating them into enterprise applications.

Key Responsibilities :

- Design, develop, and maintain scalable data pipelines for machine learning and analytics workloads.

- Build and deploy end-to-end machine learning pipelines for data ingestion, feature engineering, model training, validation, deployment, and monitoring.

- Develop and optimize ML models using frameworks such as PyTorch or TensorFlow.

- Process and transform large-scale datasets using Apache Spark, Databricks, or similar distributed data processing platforms.

- Design efficient data models and write optimized SQL queries for analytics and model training.

- Implement feature engineering, data validation, and data quality processes.

- Develop and maintain ML experimentation frameworks to support rapid model development and evaluation.

- Implement MLOps best practices, including model versioning, CI/CD pipelines, automated deployments, monitoring, and retraining workflows.

- Integrate machine learning pipelines with enterprise applications, APIs, and cloud-native platforms.

- Collaborate with data scientists, software engineers, and business stakeholders to translate business requirements into scalable AI solutions.

- Monitor production ML systems, troubleshoot issues, and optimize performance, scalability, and reliability.

- Maintain technical documentation and follow engineering best practices throughout the ML lifecycle.

Required Skills & Qualifications :

- Bachelor's degree in Computer Science, Information Technology, Data Science, Engineering, or a related field.

- Strong proficiency in Python for data engineering and machine learning.

- Hands-on experience with PyTorch, TensorFlow, or equivalent ML frameworks.

- Strong experience with Apache Spark, Databricks, or distributed data processing technologies.

- Expertise in SQL, data modeling, and relational databases.

- Solid understanding of machine learning lifecycle management and MLOps concepts.

- Experience building scalable data pipelines and production-grade ML workflows.

- Familiarity with cloud platforms such as AWS, Azure, or GCP.

- Experience with Git, CI/CD pipelines, Docker, and containerized deployments.

- Strong analytical, problem-solving, and communication skills.

Preferred Skills :

- Experience with ML orchestration tools such as MLflow, Kubeflow, Airflow, or Vertex AI.

- Knowledge of vector databases, Generative AI, or LLM-based applications.

- Experience with Kubernetes and cloud-native deployment architectures.

- Exposure to streaming platforms such as Kafka or Spark Streaming.