Key skills
AWSAzureCloudDockerGoogle Cloud PlatformKubernetesPythonSQLAIMachine LearningMLDeep LearningNLPNatural Language ProcessingGenerative AILLMLarge Language ModelsMLOpsData EngineeringGCPGoogle Cloud
About this role
Role Overview
- 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.
Requirements
- Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, or a related quantitative field.
- Minimum 3+ years of hands-on experience in an AI/ML or data science role delivering production-deployed solutions.
- Strong proficiency in Python and SQL; experience building scalable ML/NLP workflows.
- Deep hands-on experience with machine learning, deep learning, and natural language processing.
- Experience working with Generative AI and Large Language Models, including fine-tuning and evaluation techniques.
- Working knowledge of data preprocessing, feature engineering, and model validation practices.
- Experience deploying AI solutions in cloud environments (Azure, AWS, or GCP).
- Familiarity with containerization and orchestration tools (e.g., Docker, Kubernetes).
Tech Stack
- AWS
- Azure
- Cloud
- Docker
- Google Cloud Platform
- Kubernetes
- Python
- SQL
Benefits
- Health insurance
- Retirement plans
- Paid time off
- Flexible work arrangements
- Professional development
- Bonuses
- Stock options
- Equipment allowances
- Wellness programs