Own your future:
Our culture isn't something people join, it's something they build and shape. We believe that every person deserves to be heard and empowered. If you're on the fence about whether you're a fit, we say go for it. Let’s build something great together.
As an AI Engineer, you will be responsible for building and enhancing AI capabilities that address complex product challenges. You will collaborate closely with product, UI engineer, data scientists, data engineers and DevOps to design, develop, and deploy optimal AI solutions. Leveraging your expertise in statistics and software engineering, you will translate data-driven insights into scalable, high-performance AI systems that drive innovation and efficiency in clinical trial operations.
Must Haves:
- 5–6 years of experience in Data Science, AI Engineering, or ML Engineering roles.
- Advanced proficiency in Python (NumPy, Pandas, PySpark, Scikit-learn, Keras etc.) and SQL.
- Ability to write clean, efficient, and scalable code for AI model development and integration.
- Hands-on experience with developing ML models, fine tuning LLMs, prompt engineering and using RAG with proprietary LLM models.
- Understanding of model evaluation, hyper parameter tuning, and experiment tracking.
- Working knowledge of cloud platforms such as AWS, Azure, or Google Cloud Platform (GCP).
- Proficient with Git for version control and collaborative development.
- Strong foundation in statistics, probability, linear algebra, and calculus
Nice to Have:
- Experience deploying AI models using Docker and Kubernetes.
- Familiarity with AIOps practices for automating the AI lifecycle.
- Familiarity with clinical trials or healthcare domains.
Key Responsibilities:
- Design and develop AI solutions by devising optimal approaches, defining model architecture, and developing AI models.
- Collaborate with cross-functional teams to design, develop, and deploy optimal AI solutions.
- Perform data analysis and preprocessing of datasets to ensure high quality inputs for model training.
- Maintain AI models, monitor model performance, and retrain models as needed.
- Stay updated with the latest AI trends, tools, and frameworks to continuously improve existing solutions.
