AI/Machine Learning Engineer Full-time

Position Summary:Are you an AI/ML Engineer who thrives on crafting and executing cutting-edge solutions that drive value on a large scale? If so, our client is seeking an AI/ML Engineer to join their team.

In this role, you’ll collaborate closely with data scientists, engineers, and stakeholders to architect, deploy, and operationalize advanced AI/ML systems tackling intricate business challenges. Your responsibilities will span the full spectrum of the machine learning lifecycle, from conceptualization and experimentation to continuous integration/continuous deployment (CI/CD) pipelines, and ongoing model monitoring and refinement in live environments. Leveraging cloud-based AI/ML platforms, containerization, and automation, you’ll innovate MLOps frameworks and processes to streamline workflows.

Your expertise will be pivotal in optimizing AI/ML solutions for performance, scalability, and cost efficiency. You’ll deploy models via microservices, APIs, and batch scoring pipelines integrated within data products and business applications.

To excel in this role, you should possess robust proficiency in AI/ML platform engineering, contemporary data platforms, model deployment pipelines, cloud platforms, and programming languages such as Python. Exceptional problem-solving skills, attention to detail, and strong communication abilities are essential.

If you’re driven by the prospect of pushing the boundaries of artificial intelligence and delivering impactful ML solutions, seize this opportunity to shape the future of AI-driven products and services.

Responsibilities:

• Collaborate with stakeholders and data scientists to translate business requirements into actionable ML solutions.
• Engineer end-to-end AI/ML systems, from initial prototyping to production deployment.
• Design and implement AI/ML pipelines for data processing, model training, evaluation, and inference.
• Select and apply appropriate ML algorithms and frameworks (e.g., TensorFlow, PyTorch, Keras) for model development.
• Enhance model performance, accuracy, and fairness through techniques like hyperparameter tuning and error analysis.
• Deploy and maintain models using REST APIs, serverless functions, or microservices.
• Monitor and optimize AI/ML solutions using best practices and tools within the AI/MLOps domain.
• Improve model scalability, performance, and cost efficiency leveraging cloud AI/ML platforms, containerization, and automation.
• Cultivate AI/MLOps practices and foster a culture of continuous improvement.

Qualifications:Education & Certificates:

• Bachelor’s degree in Computer Science, Information Technology, or a related field.
• Industry certifications in Cloud and AI/ML Engineering are desirable.

Professional Experience:

• Minimum of 5 years of direct experience in AI/ML engineering projects.
• Experience with LLM refinement and vector database embeddings.
• Proficiency in training, evaluating, and deploying deep learning models.

Competencies & Attributes:

• Proficiency with popular ML and data platforms (e.g., AzureML, Amazon SageMaker, Databricks, Snowflake).
• Familiarity with AI/ML pipelines, AI/MLOps concepts, and relevant tools.
• Ability to develop production-grade AI/ML solutions with scalability in mind.
• Experience with MLOps tools and techniques for optimizing ML lifecycle management.
• Familiarity with ML metadata and artifact tracking platforms like MLflow.
• Experience containerizing and deploying models on cloud platforms such as Azure or AWS.
• Understanding of model governance concepts including model risk analysis and compliance.
• Proficiency in creating ML technical architecture diagrams and supporting end-to-end ML platforms.
• Experience maintaining ML pipelines and systems, assessing technical debt, and ensuring system updates.
• Strong proficiency in Python for analytics and ML applications.
• Familiarity with common Python data analysis libraries (e.g., NumPy, Pandas, SciPy) and ML libraries (e.g., Scikit-Learn, TensorFlow, PyTorch).
• Experience using Jupyter Notebooks for ML experimentation and prototyping.
• Ability to transition ML prototypes into production solutions.
• Experience with Terraform for Infrastructure as Code (IaC) of ML infrastructure on Azure or AWS cloud platforms.
• Strong problem-solving, analytical, and communication skills.

Note: Only candidates selected for interviews will be contacted.

This client operates within a diverse and inclusive environment, valuing a variety of perspectives to drive better performance. They actively seek individuals who bring unique viewpoints to their teams, fostering a culture of exchange and leveraging diversity as a competitive advantage.