As an AI/ML Engineer, you will be at the forefront of designing, developing, and deploying AI and ML solutions that drive business value and innovation.
Requirements
- Proficiency in programming languages like Python, R, and SQL, with experience in building scalable and efficient ML/NLP workflows.
- Deep understanding and hands on experience in Machine Learning, Deep Learning, and Natural Language Processing.
- Experience in working with state-of-the-art Generative AI models, including model finetuning and evaluation techniques.
- Data management knowledge, including data pre-processing, augmentation, and generation of synthetic data. This involves cleaning, labeling, and augmenting data to train and improve AI models.
- Cloud computing and deployment knowledge for deploying and managing AI applications on cloud platforms like AWS, Google Cloud, or Microsoft Azure. This includes understanding containerization technologies like Docker and orchestration tools like Kubernetes, which are important for scaling AI solutions.
Responsibilities
- Develop, optimize, and deploy machine learning and deep learning models, including LLMs and Generative AI models.
- Design and implement scalable, production-ready AI solutions that integrate into existing systems.
- Fine-tune and evaluate models on large-scale datasets to improve accuracy, efficiency, and robustness.
- Collaborate with software engineers, data scientists, and product teams to align AI models with business needs.
- Work with healthcare and claims data to build high-performance AI systems.
- Develop and maintain MLOps pipelines for training, deploying, and monitoring AI models.
- Troubleshoot and resolve AI/ML engineering challenges, ensuring models perform effectively in production.
Other
- Minimum of 3+ years’ experience in data science/ML role, with a track record of delivering impactful solutions in a production environment.
- Problem solving for thinking outside the box to design and implement novel AI solutions when faced with unprecedented challenges.
- Collaboration and communication for articulating technical details and project needs to technical and non-technical team members, including data scientists, software developers, and business stakeholders, and ability to influence leaders across functions and companies
- Continuous learning for keeping up the latest research, tools, and techniques in the rapidly evolving generative AI landscape.
- Excellent interpersonal and communication skills, with strong written and verbal presentation
