GenAI/ML Architect Pittsburgh, PA $$$ bonus

GenAI/ML Architect, machine learning frameworks (TensorFlow, PyTorch, Scikit-learn) and cloud platforms (AWS, GCP, Azure), MLOps tools (Kubeflow, MLflow, Airflow, Docker, Kubernetes)

If you post this job on a job board, please do not use company name or salary. Experience level: Mid-senior Experience required: 10 Years Education level: Bachelors degree Job function: Information Technology Industry: Information Technology and Services Pay rate : $57 per hour Total position: 1 Relocation assistance: No Visa sponsorship eligibility: No

Role GenAI/ML Architect

Business Vertical: Life Sciences, Health Care, Energy, Resources and Utilities

Responsibility:

Design and architect end-to-end AI/ML solutions, ensuring scalability, security, and efficiency.

Guide data scientists and engineers in developing, training, and deploying machine learning models.

Define best practices for MLOps, including model versioning, monitoring, and retraining strategies.

Develop AI frameworks and reusable components to accelerate AI adoption across the organization.

Collaborate with stakeholders to understand business requirements and align AI solutions accordingly.

Optimize data pipelines and AI infrastructure to support high-performance model training and inference.

Evaluate emerging AI technologies and recommend suitable tools, frameworks, and methodologies.

Ensure compliance with AI ethics, governance, and data privacy regulations.

Implement microservices architecture to build scalable and resilient software solutions.

Use Cloud platforms like AWS, Azure to deploy and run software applications.

Key Skills:

12+ years of experience in AI/ML engineering, including at least 3 years in an architectural role

Extensive experience in AI/ML model development, deployment, and lifecycle management.

Expertise in machine learning frameworks (TensorFlow, PyTorch, Scikit-learn) and cloud platforms (AWS, GCP, Azure).

Strong programming skills in Python, Java, or C++

Proficiency in MLOps tools (Kubeflow, MLflow, Airflow, Docker, Kubernetes).

Deep understanding of distributed computing, big data technologies (Spark, Hadoop), and scalable data pipelines.

Experience with NLP, deep learning, reinforcement learning, generative AI

Experience in AI-driven business transformation and enterprise AI strategies.

Familiarity with edge AI, IoT, or real-time AI processing.

Knowledge of ethical AI frameworks and responsible AI principles.

Strong problem-solving skills and ability to mentor AI/ML teams.

Experience in agile development methodologies to deliver solutions and product features.