Machine Learning Engineer

Catalyst Clinical Research provides customizable solutions to the biopharmaceutical and biotechnology industries through Catalyst Oncology, a full-service oncology CRO, and multi-therapeutic global functional and CRO services through Catalyst Flex. The company's customer-centric flexible service model, innovative technology, expert team members, and global presence advance clinical studies. Visit CatalystCR.com.

The Machine Learning Engineer is a pivotal contributor responsible for designing and implementing cutting-edge machine learning solutions with a focus on generative AI technologies. You will drive the development and deployment of advanced models and pipelines that enable the creation of AI-driven applications and enhance organizational decision-making capabilities. Additionally, you will support data engineering initiatives to enable utilization of data across the organization. Collaborating closely with internal and external stakeholders, you will translate complex requirements into innovative solutions that advance Catalyst's AI strategies while ensuring alignment with broader enterprise goals.

Position Responsibilities/Accountabilities

• Design, build, and optimize machine learning workflows, with a focus on generative AI models such as large language models (LLMs) and diffusion-based architectures.
• Develop and deploy scalable machine learning pipelines using frameworks like TensorFlow, PyTorch, and Databricks MLflow.
• Develop AI solutions using tools like Azure AI/Copilot Studio and Databricks AI Builder.
• Lead the creation of domain-specific generative AI models, ensuring ethical AI practices and bias mitigation throughout the model lifecycle.
• Design, build, and maintain scalable data pipelines with Delta Live Tables for model integration into enterprise applications.
• Enhance and expand CI/CD strategies for automated testing, model monitoring, and continuous delivery of ML artifacts.
• Manage data preprocessing, feature engineering, and synthetic data generation for machine learning use cases.
• Collaborate with cross-functional teams to align AI-driven solutions with business goals and ensure high availability for end-to-end systems.
• Provide technical expertise in the exploration of novel generative AI methods, tools, and frameworks.
• Support team members in understanding data science and AI best practices, encouraging a culture of innovation and continuous learning. Represent AI as a key member of the Data & Architecture Review Committee.

Position Qualification Requirements

Education

: B.S. or M.S. Computer Science, Engineering, Economics, Mathematics, related field, or relevant experience.

Experience

• 5+ years of experience in machine learning engineering, including model development and deployment.
• Hands-on experience with generative AI models (e.g., GPT, GANs, VAEs) and frameworks like PyTorch or TensorFlow.
• 5+ years of experience with cloud computing technologies (Azure, AWS, GCP), especially AI and ML services.
• Proficiency in developing data pipelines and integrating ML models into production environments.
• Expertise in model evaluation and monitoring, including techniques for explainability and fairness in AI.
• Experience collaborating with DevOps and MLOps teams to ensure scalability and reliability of AI solutions.
• Familiarity with project management tools such as JIRA.

Required Skills

• Advanced proficiency in Python or PySpark for ML applications.
• Deep understanding of generative AI principles, model architecture, and training methodologies.
• Expertise in large-scale data processing and engineering using Spark, Kafka, and Databricks.
• Proficiency with big data technologies and data structures like delta, parquet, YAML, JSON, and HTML.
• Strong knowledge of cloud-based AI platforms (e.g. Databricks, Azure ML, etc).
• Solid understanding of machine learning pipelines and MLOps practices.
• Exceptional problem-solving and analytical skills.
• Ability to manage priorities and workflow effectively.
• Proven ability to handle multiple projects and meet tight deadlines.
• Strong interpersonal skills with an ability to work collaboratively across teams.
• Commitment to excellence and high standards.
• Creative, flexible, and innovative team player.
• Ability to work independently and as part of various committees and teams.

Nice to Have:

• Data Engineering experience, including Webhooks, API, ELT/ETL, rETL, Data Lakehouse Architecture, and Event-Driven Architectures.
• Familiarity with deep learning frameworks for generative AI (e.g., Hugging Face Transformers).
• Knowledge of synthetic data generation techniques and tools.
• Experience with data visualization tools (e.g., Tableau, Power BI) for AI model interpretability.
• Familiarity with ethical AI principles, including explainability and bias reduction strategies.
• Experience with containerization and orchestration tools like Docker and Kubernetes.

Background or familiarity with clinical trials or pharmaceutical development.

Working Hours

• Everyday: 1:30 PM - 9:00 PM IST

OR

• Monday, Wednesday, Friday: 2:30 PM - 10:30 PM IST
• Tuesday, Thursday: 9:00 AM - 5:00 PM IST

Note: Working hours may vary based on individual seniority, business demand, and ability to work independently. This will be evaluated on a case-by-case basis.