AI Architect

TATA Consulting Services•Wilmington, MA

35d•$110,000 - $140,000

About The Position

Lead the complete development and deployment lifecycle of generative AI solutions on Azure and AWS, ensuring they align with business objectives and technical requirements. Develop and define the AI strategy and roadmap, emphasizing generative AI capabilities and their seamless integration into products and services. Supervise the design, fine-tuning, and optimization of large language models (LLMs) and other generative AI models across various applications. Manage and mentor a diverse team of AI developers and engineers, promoting a culture of collaboration and innovation. Work closely with cross-functional teams - engineering, and business stakeholders to translate requirements into effective AI solutions. Uphold best practices in AI ethics, data privacy, and model governance throughout the AI development lifecycle. Lead research and innovation efforts by keeping current with the latest dev elopments in generative AI, multimodal models, and AI agents. Implement containerization and orchestration strategies using Docker and Kubernetes for reliable deployment. Establish security frameworks using Azure/AWS Identity, and other tools to ensure restricted and compliant access to AI systems. Monitor AI model performance in production, drive continuous improvement initiatives, and manage risks associated with AI outputs. Effectively communicate AI concepts, project progress, and business impact to both technical and non-technical audiences, including executive leadership. Knowledge of Cloud Storage, Cloud SQL, including their use cases and configurations. Knowledge of real-time messaging and event-driven architectures like pub/sub. Familiarity with Cloud Functions, Cloud Run, and App Engine, and how to deploy and manage serverless applications. Familiarity with Azure, AWS Cloud's Operations Suite, including Monitoring, Logging, Trace, Debugger, and Error Reporting to ensure system reliability and performance.

Requirements

6+ years of experience in AI/ML development, with significant focus on generative AI technologies on Azure, AWS
Proven leadership experience managing AI teams and delivering complex AI projects.
Strong expertise in Python and machine learning frameworks such as TensorFlow, PyTorch, and Hugging Face Transformers.
Deep understanding of generative AI techniques including large language models, GANs, VAEs, diffusion models, and AI agents.
Experience with Azure, AWS and AI model deployment at scale.
Excellent problem-solving, project management, and communication skills.
Ability to drive innovation and influence strategic decisions.
Experience with AI agents.
Familiarity with multimodal AI models (text, image, audio).
Knowledge of Cloud Storage, Cloud SQL, including their use cases and configurations.
Knowledge of real-time messaging and event-driven architectures like pub/sub.
Familiarity with Cloud Functions, Cloud Run, and App Engine, and how to deploy and manage serverless applications.
Familiarity with Azure, AWS Cloud's Operations Suite, including Monitoring, Logging, Trace, Debugger, and Error Reporting to ensure system reliability and performance.

Responsibilities

Lead the complete development and deployment lifecycle of generative AI solutions on Azure and AWS, ensuring they align with business objectives and technical requirements.
Develop and define the AI strategy and roadmap, emphasizing generative AI capabilities and their seamless integration into products and services.
Supervise the design, fine-tuning, and optimization of large language models (LLMs) and other generative AI models across various applications.
Manage and mentor a diverse team of AI developers and engineers, promoting a culture of collaboration and innovation.
Work closely with cross-functional teams - engineering, and business stakeholders to translate requirements into effective AI solutions.
Uphold best practices in AI ethics, data privacy, and model governance throughout the AI development lifecycle.
Lead research and innovation efforts by keeping current with the latest dev elopments in generative AI, multimodal models, and AI agents.
Implement containerization and orchestration strategies using Docker and Kubernetes for reliable deployment.
Establish security frameworks using Azure/AWS Identity, and other tools to ensure restricted and compliant access to AI systems.
Monitor AI model performance in production, drive continuous improvement initiatives, and manage risks associated with AI outputs.
Effectively communicate AI concepts, project progress, and business impact to both technical and non-technical audiences, including executive leadership.
Knowledge of Cloud Storage, Cloud SQL, including their use cases and configurations.
Knowledge of real-time messaging and event-driven architectures like pub/sub.
Familiarity with Cloud Functions, Cloud Run, and App Engine, and how to deploy and manage serverless applications.
Familiarity with Azure, AWS Cloud's Operations Suite, including Monitoring, Logging, Trace, Debugger, and Error Reporting to ensure system reliability and performance.