GenAI/ML Architect

Together We Talent-posted 2 days ago

Mid Level

Onsite • Pittsburgh, PA

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A leading global technology firm is seeking a GenAI/ML Architect to drive the design and deployment of advanced AI and machine learning solutions across complex enterprise environments. This role combines deep technical expertise in ML architecture, cloud platforms, and MLOps with a strategic understanding of how to apply Generative AI to real -world business challenges. This position is 100% onsite in Pittsburgh, PA.

Architect and Design: Build end -to -end AI/ML architectures that support large -scale, data -intensive applications.
Lead and Mentor: Guide teams of data scientists and engineers in developing, training, and deploying ML models and pipelines.
Generative AI Integration: Implement and optimize GenAI solutions, including NLP, deep learning, and reinforcement learning models.
MLOps & Automation: Define and enforce best practices for model deployment, versioning, monitoring, retraining, and governance.
Infrastructure Optimization: Design scalable data pipelines and distributed systems for high -performance model training and inference.
Innovation & Evaluation: Assess and implement emerging AI technologies, frameworks, and tools to accelerate innovation.
Compliance & Ethics: Ensure AI systems align with governance, data privacy, and ethical AI principles.
Cloud Deployment: Deploy and manage AI/ML workloads on AWS, GCP, and Azure, leveraging microservices and containerized environments.
Cross -Functional Collaboration: Partner with business, data, and infrastructure teams to align AI solutions with strategic enterprise objectives.

Bachelor’s degree in Computer Science, Engineering, or a related technical field.
12+ years of experience in AI/ML engineering, with 3+ years in an architectural role.
Proven expertise in machine learning frameworks such as TensorFlow, PyTorch, and Scikit -learn.
Hands -on experience with Generative AI, NLP, deep learning, and reinforcement learning.
Proficiency in Python, Java, or C++ for model development and integration.
Strong knowledge of MLOps tools such as Kubeflow, MLflow, Airflow, Docker, and Kubernetes.
Experience with big data technologies (Spark, Hadoop) and distributed computing frameworks.
Skilled in building and deploying models on cloud platforms (AWS, GCP, Azure).
Familiarity with Edge AI, IoT, and real -time inference pipelines.
Understanding of ethical and responsible AI frameworks.
Experience applying Agile methodologies for rapid, iterative delivery.

Background in Life Sciences, Healthcare, Energy, or Utilities industries.
Experience with AI -driven digital transformation and enterprise AI strategy.
Strong analytical, leadership, and mentoring skills.
Excellent communication and stakeholder management abilities.

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