What are the responsibilities and job description for the AI/ML Engineering Manager position at Turn2Partners?
Position Summary:
Are you an AI/ML Engineer who loves building and implementing innovative solutions that create value at scale? If so, you may be the perfect fit for the Senior AI/ML Engineer role.
In this role, you will collaborate with data scientists, engineers, and stakeholders to design, deploy, and operationalize state-of-the-art AI/ML systems. You’ll drive the innovation of MLOps platforms and processes for the full machine learning lifecycle—from model experimentation to CI/CD pipelines to model monitoring and retraining in production environments.
You will leverage cloud AI/ML platforms, containerization, and automation tools to streamline AI/ML workflows. Additionally, you will optimize AI/ML solutions for performance, scalability, and cost, and serve models via microservices, APIs, and batch scoring pipelines integrated with data products and business applications.
Primary Responsibilities:
- Collaborate with stakeholders and data scientists to translate business problems and requirements into ML solutions
- Engineer end-to-end AI/ML systems from prototyping to production deployment
- Design and implement AI/ML pipelines for data ingestion, transformation, model training, evaluation, and inference
- Choose and apply suitable ML algorithms and frameworks (e.g., TensorFlow, PyTorch, Keras) for model development
- Optimize model performance, accuracy, and fairness through techniques like hyperparameter tuning, error analysis, and model governance
- Deploy and serve models using REST APIs, serverless functions, or microservices
- Monitor and maintain AI/ML solutions using AI/MLOps best practices and tools
- Enhance model scalability, performance, and cost efficiency using cloud AI/ML platforms, containerization, and automation
- Build AI/MLOps discipline and practice
Competencies & Attributes:
- Proficiency with common ML and data platforms such as AzureML, Amazon SageMaker, Databricks, and Snowflake
- Knowledge of AI/ML pipelines, AI/MLOps concepts, and tools
- Ability to build production-grade AI/ML solutions with scalability in mind
- Experience with MLOps tools and techniques to optimize ML lifecycle management
- Experience with ML metadata and artifact tracking platforms like MLflow
- Experience containerizing and deploying models to cloud platforms like Azure or AWS
- Understanding of model governance concepts such as model risk analysis, QA, compliance
- Experience with building ML technical architecture diagrams encompassing data, model building, and operations
- Experience operating end-to-end ML platforms supporting analytics and ML teams
- Experience assessing model technical debt, maintaining pipelines, and keeping systems up-to-date
- Experience with Python for analytics and ML applications
- Proficiency with common Python data analysis libraries like NumPy, Pandas, SciPy
- Experience with common Python ML libraries like Scikit-Learn, TensorFlow, PyTorch
- Experience with Jupyter Notebooks for ML experimentation and prototyping
- Ability to transition ML prototypes to production solutions
- Experience with Terraform for Infrastructure as Code (IaC) of ML infrastructure on Azure, AWS cloud platforms
- Strong problem-solving, analytical, and communication skills
