GenAI/ML Architect

BELEN Network

Pittsburgh (Allegheny County)

On-site

USD 75,768 - 89,544

Full time

14 days+

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Job summary

Join a forward-thinking company as a GenAI/ML Architect, where you will design and deploy cutting-edge AI solutions across various regulated industries. This role is perfect for a strategic technologist eager to lead innovative initiatives while ensuring compliance and scalability. You will collaborate with data teams to transform business objectives into AI-powered solutions, define architectural best practices, and integrate robust MLOps frameworks. If you are passionate about AI and want to make a significant impact, this is the opportunity for you!

Qualifications

12+ years in AI/ML engineering, with 3+ years as an architect.
Experience leading cross-functional teams and mentoring.

Responsibilities

Architect end-to-end AI/ML solutions ensuring they are production-ready.
Guide data teams in developing and deploying ML models.

Skills

GenAI/ML Architecture

Machine Learning Frameworks

Cloud Platforms

MLOps Tools

Programming Languages

AI Specializations

Infrastructure & Big Data

AI Governance

Tools

TensorFlow

PyTorch

Scikit-learn

AWS

GCP

Azure

Kubeflow

MLflow

Airflow

Docker

Kubernetes

Job description

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This range is provided by BELEN Network. Your actual pay will be based on your skills and experience — talk with your recruiter to learn more.

Base pay range

$55.00/hr - $65.00/hr

We are seeking a forward-thinking GenAI / ML Architect to design and deploy enterprise-grade AI/ML solutions across regulated industries including Life Sciences, Healthcare, Energy, and Utilities. This role is ideal for a strategic technologist who can lead cutting-edge GenAI initiatives while ensuring compliance, scalability, and performance in real-world environments.

You’ll play a pivotal role in transforming business objectives into scalable AI-powered solutions—guiding data teams, defining architectural best practices, and integrating robust MLOps frameworks.

Key Responsibilities

Architect end-to-end AI/ML and Generative AI solutions, ensuring they are production-ready, secure, and scalable.
Guide data scientists and engineers in developing, training, and deploying ML models using state-of-the-art frameworks.
Define and implement MLOps best practices, including model versioning, CI/CD pipelines, monitoring, and retraining strategies.
Develop reusable GenAI frameworks, toolkits, and components for rapid deployment across use cases.
Collaborate with business stakeholders to translate requirements into AI-enabled solutions aligned with industry needs.
Optimize cloud infrastructure and data pipelines to support model training and low-latency inference.
Evaluate and integrate emerging GenAI tools, responsible AI practices, and compliance standards.
Implement microservices architecture for scalable, modular AI systems.
Deploy applications using cloud-native services on AWS, GCP, and Azure.

Core Skills & Technologies

GenAI/ML Architecture – Ability to lead solution architecture from POC to production in enterprise environments.
Machine Learning Frameworks – Deep expertise in TensorFlow, PyTorch, Scikit-learn.
Cloud Platforms – Hands-on deployment experience with AWS, GCP, Azure.
MLOps Tools – Skilled in Kubeflow, MLflow, Airflow, plus containerization and orchestration with Docker and Kubernetes.
Programming Languages – Advanced in Python, and proficient in Java or C++.
AI Specializations – Practical knowledge of deep learning, NLP, reinforcement learning, and Generative AI models.
Infrastructure & Big Data – Strong grasp of distributed systems, Spark, Hadoop, and scalable data engineering pipelines.
AI Governance – Familiar with ethical AI frameworks and privacy compliance regulations.

Qualifications

12+ years of experience in AI/ML engineering, with 3+ years in an AI/ML architect role.
Proven success in enterprise AI adoption and digital transformation projects.
Experience leading cross-functional teams and mentoring junior engineers and data scientists.
Strong background in agile methodologies and collaborative development environments.

Skills: data,pipelines,programming languages,tensorflow,cloud platforms,ai governance,genai/ml architecture,machine learning frameworks,ml,azure,mlflow,airflow,compliance,gen/ai,gcp,architecture,cloud,kubernetes,enterprise,learning,ai specializations,aws,pytorch,mlops tools,infrastructure & big data

Seniority level