AI/ML Engineer

Are you an AI/ML Engineer who loves to build and implement innovative solutions that create value at scale? If so, you might be the perfect fit for our Senior AI/ML engineer role at Carlyle.

In this role, you will work with data scientists, engineers, and stakeholders to design, deploy, and operationalize state-of-the-art AI/ML systems that solve complex business problems. You will also 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, automation tools and processes to streamline AI/ML workflows.

Additionally, you will optimize AI/ML solutions for performance, scalability and cost. You will serve models via microservices, APIs and batch scoring pipelines integrated with data products and business applications.

You should have strong expertise in AI/ML platform engineering, modern data platforms, model deployment pipelines, relevant cloud platforms and programming languages like Python. You should also have excellent problem-solving abilities, attention to detail and communication skills.

If you are passionate about pushing the boundaries of artificial intelligence and making an impact by delivering innovative ML solutions, this is the role for you. Join us and help shape the future of AI-driven products and services at Carlyle.

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 such as TensorFlow, PyTorch, Keras for model development
• Optimize model performance, accuracy and fairness using 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

Requirements:

Education &Certificates

• Bachelor’s degree, required
• Concentration in Computer Science, Information Technology, or related field, preferred
• Industry Cloud and AI/ML Engineering level certifications desired

Professional Experience

• Minimum of 6 years of overall relevant technical experience, required
• 5+ years of direct experience in AI/ML engineering projects, preferred
• Experience with LLM refinement and vector database embeddings
• Experience with training, evaluating and deploying deep learning models

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 such as MLflow
• Experience containerizing and deploying models and solutions to cloud platforms like Azure or AWS
• Understanding of model governance concepts such model risk analysis, QA, compliance
• Experience with building ML technical architecture diagrams encompassing data, model building, operations
• Experience with operating end-to-end ML platforms supporting analytics and ML teams
• Experience with assessing model technical debt, maintaining pipelines, 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 IaC of ML infrastructure on Azure, AWS cloud platforms.
• Strong problem solving, analytical and communication skills