We are seeking a talented
AI / ML Engineer to design, develop, and deploy
machine learning and
AI-driven applications on
AWS Cloud. The role involves building scalable ML pipelines, training and tuning models, and operationalizing AI solutions that enhance data-driven decision-making across research, clinical, and enterprise use cases.
Key Responsibilities
- Design and develop machine learning models for predictive analytics, classification, and anomaly detection.
- Implement NLP and computer vision solutions for scientific, clinical, and document intelligence use cases.
- Build and manage end-to-end MLOps pipelines for model training, deployment, and monitoring.
- Integrate AI models into business applications and APIs for seamless consumption by data products.
- Monitor model performance, retrain as necessary, and ensure explainability and compliance with governance standards.
- Work collaboratively with data engineers, data scientists, and DevOps teams to optimize workflows.
- Ensure adherence to HIPAA, GxP, and 21 CFR Part 11 data compliance standards in AI environments.
Technical Skills
- Core ML & AI Services: Amazon SageMaker, SageMaker Pipelines, SageMaker Studio
- Generative AI & NLP: Amazon Bedrock, Amazon Comprehend Medical, Amazon Textract, Hugging Face
- Data Processing & Automation: AWS Glue, Lambda, Step Functions, EventBridge
- Model Deployment: API Gateway, ECS/EKS for scalable inference endpoints
- Proficiency in Python, with experience using TensorFlow, PyTorch, Scikit-learn, or Hugging Face Transformers
- Strong understanding of MLOps, model monitoring, and CI/CD for ML workflows
- Familiarity with AWS Security tools (IAM, KMS, CloudTrail) and governance frameworks
Qualifications & Experience
- Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, or a related field
- 5+ years of experience in machine learning engineering, data science, or AI solution development
- Proven experience building and deploying production-grade ML models on AWS
- Strong understanding of ML lifecycle management, data preprocessing, and feature engineering
- Experience collaborating in Agile environments with cross-functional data and engineering teams
Preferred Qualifications
- Experience in life sciences, healthcare, or pharmaceutical analytics
- Familiarity with bioinformatics or clinical data modeling
- AWS Certified Machine Learning Specialty or AWS Certified AI Practitioner
- Knowledge of AI ethics, model explainability, and responsible AI principles
