AI/ML Engineer

Job Description:

As an AI/ML Engineer, you will design, develop, and deploy machine learning models and AI

systems to solve complex business challenges. Youll work closely with cross-functional teams,

including data scientists, software engineers, and product managers, to build scalable and

robust solutions that drive value and innovation.

Requirements:

• Experience building end-to-end machine learning infrastructure for large and complex systems.
• Expertise in container tools like Docker and orchestration systems like Kubernetes.
• Solid understanding of software engineering practices, including version control (Git) and collaborative development workflows.
• Hands-on experience with DevOps tools, CI/CD pipelines, and deployment practices.
• Proven experience leading technical teams to deliver successful projects and innovation.
• Strong problem-solving skills to tackle complex technical challenges.
• Excellent communication and collaboration skills for working across teams and stakeholders.
• Design and build machine learning pipelines using tools like Databricks, PySpark, AWS SageMaker, and Python to train and deploy models.
• Connect models to databases like MongoDB, MariaDB, and DynamoDB for storing and retrieving data.
• Ensure data flows smoothly between NoSQL databases and SQL databases
• Deploy ML models using AWS SageMaker or FastAPI to make them available for real use with high speed and reliability.
• Set up tools like New Relic to monitor the models and ensure they are working correctly.
• Work with data scientists to turn research models into real-world systems.
• Mentor junior engineers on building machine learning systems and using tools like FastAPI.

Technical Attributes:

• Strong skills in Python, PySpark, and cloud services like AWS, S3, DynamoDB, andSageMaker.
• Experience with Docker and Kubernetes for deployment.
• Familiarity with monitoring tools like New Relic and databases like MongoDB, MariaDB, and DynamoDB.
• Strong skills in Python, ML Frameworks like TensorFlow or PyTorch
• Experience with Docker and Kubernetes for deployment.
• Proficiency with cloud platforms such as AWS or Azure for creating ML systems.