Ai Ml Engineer

Responsibilities:

• Build and automate robust, scalable ML pipelines for training, validation,

deployment, and monitoring in production environments.

• Collaborate with data scientists, engineers, and product managers to

understand business requirements and translate them into ML solutions.

• Analyze and process large-scale datasets to develop efficient ML models and

drive actionable insights.

• Own the end-to-end ML model lifecycle, including versioning, deployment,

performance monitoring (e.g., drift detection), recalibration, troubleshooting,

and retraining.

• Develop and manage CI/CD pipelines for ML workflows, including automated

testing, containerization, and model registry integration.

• Implement and manage scalable ML infrastructure for data processing,

training, and inference in collaboration with DevOps and engineering teams.

• Continuously improve engineering practices, code quality, and automation,

while staying up to date with the latest trends in AI, ML, and MLOps.

Requirements:

• Minimum 5 years of experience in ML engineering or a combination of ML

engineering and data science, with a focus on data analysis, feature

engineering, model development, deployment, and maintenance.

• Strong hands-on experience with Databricks for ML model development and

deployment.

• Strong understanding of machine learning and data science fundamentals

(e.g., supervised and unsupervised learning, feature selection, etc.).

• Proficiency with popular ML frameworks and libraries (e.g., TensorFlow,

PyTorch, Scikit-learn).

• Experience implementing MLOps practices using tools such as MLflow and

CI/CD platforms (e.g., GitHub).

• Deep understanding of the ML model lifecycle and production workflows.

• High proficiency in SQL and Python.

• Familiarity with cloud-based data platforms such as Snowflake or similar

technologies.

• Excellent communication and interpersonal skills to collaborate across teams.

• Robust problem-solving and analytical skills.