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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- 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 – 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.
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- 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 – 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.