Manager of Artificial Intelligence

Peoplefy

0 years

0 Lacs

chennai tamil nadu india

Posted:1 week ago| Platform:

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Skills Required

data ai design model scalability reliability enablement extraction summarization retrieval collaboration orchestration ml strategy governance architecture testing integration containerization tooling standardization drive development analytics management leadership engagement communication documentation visual

Work Mode

On-site

Job Type

Full Time

Job Description

Key Responsibilities

Architect Scalable Data & AI Solutions: Design and architect end-to-end data and AI systems, including data pipelines, model workflows, and AI services, ensuring scalability, reliability, and performance across enterprise use cases.
GenAI Enablement: Help Integrate Generative AI capabilities such as entity extraction, summarization, and retrieval-augmented generation (RAG) into enterprise data platforms to enrich and contextualize data assets.
Agentic AI Frameworks: Architect autonomous AI agent systems using frameworks like LangGraph, CrewAI, or similar, enabling multi-agent collaboration and orchestration for complex business tasks.
AI/ML Strategy & Governance: Define and implement AI/ML architecture standards, model lifecycle governance, and responsible AI practices including bias testing, explainability, and ethical deployment.
Cross-Functional Collaboration: Partner with Data Engineers, MLOps, and Platform teams to ensure seamless integration of AI models into production environments, including containerization, CI/CD pipelines, and monitoring.
Tooling & Platform Standardization: Drive standardization of AI development tools, libraries, and infrastructure across teams to improve productivity, maintainability, and compliance.
Advanced Analytics Enablement: Enable teams to build high-performing models by providing architectural guidance on model selection, and performance optimization.
Data Architecture Alignment: Work closely with data architects to align AI solutions with enterprise data strategy, including data lakehouse design, metadata management, and data quality frameworks.
Innovation & Thought Leadership: Stay ahead of emerging trends in AI, GenAI, and data architecture; evaluate new technologies and frameworks for adoption; and evangelize best practices across the organization.
Stakeholder Engagement: Collaborate with business leaders, product owners, and domain experts to translate strategic objectives into AI-enabled solutions and ensure alignment with business goals.
Communication: Provide architectural insights and solution roadmaps through clear documentation, visual models, and presentations to technical and non-technical stakeholders.

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