About the Role: AI Engineer
We are seeking a detail-oriented AI Engineer to manage and oversee the daily operations of our machine learning workflows and model deployment pipelines. In this role, you will act as the vital link between our data science research, software engineering teams, and cloud infrastructure partners, ensuring that all models are developed with maximum efficiency, scalability, and adherence to strict ethical AI standards and data security regulations.
Core Responsibilities
- Model Lifecycle Management: Coordinate daily AI/ML activities, including data preprocessing, model training, hyperparameter tuning, and deployment scheduling.
- Regulatory & Ethical Compliance: Maintain comprehensive model documentation and version control; ensure all algorithmic activities align with data privacy laws (GDPR/CCPA), bias mitigation frameworks, and company SOPs.
- Data Integrity: Perform accurate feature engineering and data validation; ensure "ALCOA+" principles are applied to training datasets and model performance logs.
- Risk Mitigation: Monitor model drift and computational bottlenecks; report significant performance degradation or system non-compliance promptly to management and stakeholders.
- Quality Assurance: Author and maintain model specifications; assist in internal and external audits to ensure GxP (where applicable) and ISO quality standards for automated systems are met.
- Technical Liaison: Serve as the primary point of contact for cloud service providers and third-party API vendors during performance reviews and technical audits.
Qualifications
- Education: Bachelor’s degree in Computer Science, Data Science, Artificial Intelligence, Mathematics, or a related field (Required).
- Experience: 0–5 years of experience in machine learning, software development, data engineering, or research environments.
- Technical Skills (Required):
- Strong understanding of Supervised/Unsupervised Learning, Deep Learning, and Neural Network architectures.
- Proficiency in programming languages (Python, R, C++) and ML frameworks (e.g., TensorFlow, PyTorch, Scikit-learn).
- Familiarity with cloud platforms (AWS, Azure, or GCP) and MLOps tools (e.g., Kubeflow, MLflow, Docker).
- Excellent technical writing and organizational skills.
- Preferred Skills:
- Certification (or eligibility) for AWS Certified Machine Learning or Google Professional ML Engineer.
- Experience with Natural Language Processing (NLP) or Computer Vision in regulated industries.
- Knowledge of Big Data technologies (Spark, Hadoop) and advanced data analytics.
What We Offer
- Targeted Placement: Direct marketing to our network of hiring managers in the Tech, Manufacturing, and MedTech industries.
- Technical Resume Rebuild: Optimization of your profile to highlight AI expertise alongside algorithmic efficiency.
- Interview Coaching: Guidance on technical coding interviews and machine learning system design case studies.
Ready to Take the Next Step?
Email your updated resume to: [email protected]