Enterprise Architect AI

TATA Consulting Services•Edison, NJ

52d•Onsite

About The Position

We are seeking a seasoned AI Architect with 10+ years of experience in enterprise architecture, software engineering, and AI/ML solution design. This role demands a strategic thinker and technical leader who can drive AI innovation across the organization, architect scalable solutions, and mentor cross-functional teams. The ideal candidate will have a proven track record of delivering impactful AI systems in complex environments.

Requirements

Bachelor's or master's degree in computer science, Artificial Intelligence, Data Science, or related field.
10+ years of experience in software architecture, with 5+ years focused on AI/ML.
Deep expertise in Python, TensorFlow, PyTorch, and cloud platforms (AWS, Azure, GCP).
Strong background in data engineering, distributed systems, and enterprise integration.
Experience with MLOps tools (MLflow, Kubeflow, Airflow) and containerization (Docker, Kubernetes).
Experience with generative AI, LLMs, and NLP.
Certifications in cloud architecture or AI/ML (e.g., AWS Certified Machine Learning, Azure AI Engineer).
Excellent communication, leadership, and stakeholder management skills.

Nice To Haves

Familiarity with industry-specific AI applications (e.g., manufacturing, healthcare, finance).

Responsibilities

Define and lead the enterprise-wide AI strategy and architecture roadmap.
Collaborate with executive leadership to align AI initiatives with business objectives.
Evaluate emerging AI technologies and guide adoption across business units.
Architect robust, scalable, and secure AI/ML platforms and solutions.
Lead the design of data pipelines, model training workflows, and deployment frameworks.
Integrate AI capabilities into existing enterprise systems using APIs and microservices.
Establish AI governance frameworks including model lifecycle management, auditability, and ethical AI practices.
Ensure compliance with global data privacy regulations (GDPR, CCPA, etc.).
Mentor and guide data scientists, ML engineers, and software developers.
Promote best practices in MLOps, DevOps, and cloud-native AI development.
Lead technical reviews, architecture boards, and innovation workshops.
Monitor AI systems in production and optimize for performance, scalability, and cost-efficiency.
Implement feedback loops and continuous learning systems for model improvement.