MLOps Engineer

We are looking for a MLOps Engineer with prior experience in working with Data Scientists to ensure a smooth transition of models from development to production.

Test. Deploy. Maintain. Monitor.

The opportunities to be a part of something totally new across ML, traditional AI and GenAI are virtually unlimited. This role is part of Intapp Cloud Engineering organization responsible for Cloud Architecture, Cloud Security, Networking, CICD and Cloud Operations.

As an integral part of our team, you will not only need to tackle operational challenges at every layer of the system infrastructure, but also set up essential tools like Spark, Databricks, Snowflake, Kubernetes, and Kafka for data science infrastructure.

What you will do :

Collaborate with stakeholders to define MLOps strategies aligned with business objectives and technical requirements. Assess current infrastructure, processes, and tooling to identify gaps and opportunities for MLOps implementation.

Design, Develop, and Implement end-to-end ML deployment pipelines for model training, perform validation, deployment, and monitoring.

Automate data ingestion, feature engineering, model training, and do evaluation processes using tools like Apache Airflow, Kubeflow, or MLflow.

Architect and deploy scalable infrastructure for ML workloads using cloud platforms and containerization technologies (e.

g., Docker, Kubernetes).

Implement model monitoring and logging solutions to track model performance, data drift, and model drift in production.

Perform Integration and Deployment (CI / CD), Establish CI / CD pipelines for automated testing, validation, and deploy ML models using tools like Jenkins, Azure DevOps.

Implement version control and model versioning practices to manage changes and updates to ML models.

Implement security best practices for securing ML infrastructure, data, and models in compliance with regulatory requirements.

Establish governance policies and access controls for managing and monitoring ML artifacts and resources.

Provide training and mentorship to data scientists, engineers, and stakeholders on MLOps practices, tools, and methodologies.

Foster a culture of collaboration and continuous improvement in MLOps adoption across the organization.

3-4 years of experience in data engineering, DevOps, or related field.

Proven experience with Azure Machine Learning, Azure Data Factory, and Azure DevOps.

Familiarity with machine learning concepts and model training pipelines.

Strong understanding of CI / CD best practices and automation tools.

Excellent communication and collaboration skills.

Ability to work independently and as part of a cross-functional team.

Our culture at Intapp emphasizes accountability, responsibility, and growth. We support each other in a positive, open atmosphere that fosters creativity, approachability, and teamwork.

We're committed to creating a modern work environment that's connected yet flexible, supporting both professional success and work-life balance.

In return for your passion, commitment, and collaborative approach, we offer :

Competitive base salary plus variable compensation and equity

Generous paid parental leave, including adoptive leave

Traditional comprehensive benefits, plus :

Generous Paid Time Off

Tuition reimbursement plan

Family Formation benefit offered by Carrot

Wellness programs and benefits provided by Modern Health

Paid volunteer time off and donation matching for the causes you care about

Home office stipend

Opportunities for personal growth and professional development supported by a community of talented professionals

An open, collaborative environment where your background and contributions are valued

Experience at a growing public company where you can make an impact and achieve your goals

Open offices and kitchens stocked with beverages and snacks