Responsibilities
- Design and implement machine learning, deep learning, and generative AI systems, including large language models, from concept through evaluation.
- Select and apply suitable modeling approaches—such as supervised, unsupervised, NLP, and deep learning—based on data availability and problem requirements.
- Enhance model effectiveness by balancing accuracy, response time, scalability, and operational costs.
- Evaluate models rigorously using large datasets to ensure performance and reliability.
- Improve model resilience through data cleaning, feature creation, data expansion, and synthetic data generation.
- Develop robust, scalable AI solutions that integrate seamlessly with existing software platforms and operational workflows.
- Create and maintain automated MLOps pipelines for training, deploying, monitoring, and managing machine learning models.
- Deploy AI systems in cloud environments such as Azure, AWS, or GCP, using containerization and orchestration tools when appropriate.
- Monitor live models to detect performance drift, degradation, or failures, and apply corrective actions.
- Diagnose and resolve technical issues in AI/ML systems across development and production stages.
- Collaborate with product managers, software engineers, data scientists, and research teams to ensure AI solutions meet business goals.
- Convert product needs and use cases into technical designs and model architectures.
- Enable integration of AI features into customer-facing products and internal tools.
- Explain complex technical decisions, tradeoffs, and constraints to non-technical audiences clearly.
- Work with diverse datasets—including structured and unstructured data from healthcare, insurance claims, and life sciences—to build effective AI systems.
- Ensure data used in training and inference is processed, transformed, and validated responsibly and accurately.
- Coordinate with data engineering and quality assurance teams to build auditable, production-grade data and AI pipelines.
- Stay informed about advancements in generative AI, large language model architectures, and fine-tuning methods.
- Help establish internal standards, best practices, and reusable components for machine learning development.
- Document workflows, system designs, methodologies, and insights for team-wide knowledge sharing.
- Identify and act on opportunities to enhance the scalability, reliability, and efficiency of current AI infrastructure.
Responsibilities
- Design, develop, fine-tune, and evaluate machine learning, deep learning, and Generative AI models, including Large Language Models (LLMs).
- Apply appropriate modeling techniques (supervised, unsupervised, NLP, deep learning) based on problem context and data constraints.
- Optimize model performance across accuracy, latency, scalability, and cost dimensions.
- Conduct rigorous model evaluation, validation, and benchmarking using large-scale datasets.
- Apply data preprocessing, feature engineering, augmentation, and synthetic data generation techniques to improve model robustness.
- Design and implement scalable, production-ready AI solutions integrated into existing platforms and workflows.
- Build, maintain, and improve MLOps pipelines for model training, deployment, monitoring, and lifecycle management.
- Deploy and manage AI applications in cloud environments (Azure, AWS, or GCP), including containerization and orchestration where applicable.
- Monitor model performance in production; identify drift, degradation, or failures and implement remediation strategies.
- Troubleshoot and resolve AI/ML engineering issues across development and production environments.
- Partner with Product Managers, Product Owners, Software Engineers, Data Scientists, and Research teams to align AI solutions with business and product objectives.
- Translate product requirements and use cases into technical architectures and model designs.
- Support integration of AI capabilities into customer-facing products and internal platforms.
- Communicate technical concepts, tradeoffs, and limitations clearly to non-technical stakeholders.
- Work with structured and unstructured datasets, including healthcare, claims, and life sciences data, to build high-performance AI systems.
- Ensure responsible handling, transformation, and validation of data used for model training and inference.
- Collaborate with data engineering and QA teams to ensure data pipelines and AI workflows are production-ready and auditable.
- Stay current with advances in Generative AI, LLM architectures, model fine-tuning techniques, and applied machine learning.
- Contribute to internal best practices, standards, and reusable components for AI/ML development.
- Document AI/ML workflows, architectures, methodologies, and lessons learned for internal knowledge sharing.
- Proactively identify opportunities to improve scalability, reliability, and efficiency of existing AI systems.
