Software Engineer, AI/ML

• Implement, and deploy Machine Learning solutions to solve complex problems and deliver real business value, ie. revenue, engagement, and customer satisfaction.
• Collaborate with data product managers, software engineers and SMEs to identify AI/ML opportunities for improving process efficiency.
• Develop production-grade ML models to enhance customer experience, content recommendation, content generation, and predictive analysis.
• Monitor and improve model performance via data enhancement, feature engineering, experimentation and online/offline evaluation.
• Stay up-to-date with the latest in machine learning and artificial intelligence, and influence AI/ML for the Life science industry.
• 2 - 4 years of experience in AI/ML engineering, with a track record of handling increasingly complex projects.
• Strong programming skills in Python, Rust.
• Experience with Pandas, NumPy, SciPy, OpenCV (for image processing)
• Experience with ML frameworks, such as scikit-learn, Tensorflow, PyTorch.
• Experience with GenAI tools, such as Langchain, LlamaIndex, and open source Vector DBs.
• Experience with one or more Graph DBs - Neo4J, ArangoDB
• Experience with MLOps platforms, such as Kubeflow or MLFlow.
• Expertise in one or more of the following AI/ML domains: Causal AI, Reinforcement Learning, Generative AI, NLP, Dimension Reduction, Computer Vision, Sequential Models. Expertise in building, deploying, measuring, and maintaining machine learning models to address real-world problems. Thorough understanding of software product development lifecycle, DevOps (build, continuous integration, deployment tools) and best practices. Excellent written and verbal communication skills and interpersonal skills. Advanced degree in Computer Science, Machine Learning or related field.

in the life science domain or a related field is preferable

• Expertise in building, deploying, measuring, and maintaining machine learning models to address real-world problems.
• Thorough understanding of software product development lifecycle, DevOps (build, continuous integration, deployment tools) and best practices.