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AI COE Lead (Artificial Intelligence - COE Lead)

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Onsite

AI COE Lead (Artificial Intelligence - COE Lead)

Saaki Argus And Averil Consulting

AI COE Lead (Artificial Intelligence - COE Lead)

Skills

Data SciencePyTorchTensorFlowData AnalysisMachine LearningAIAWSNLP / Natural Language ProcessingComplianceRegulatory ComplianceBusiness TransformationAzure

8 months ago

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Important information


Location:

Tamil Nadu, India

Work mode:

Onsite

Published on:

28 September 2026

What they need


About the Client:

A leading global IT services and technology distribution company.

Role: Artificial Intelligence - Centre of Excellence Lead (AI COE Lead)

Location: Chennai

Role Summary:

The AI Lead will champion the enterprise-wide adoption and strategic deployment of Artificial Intelligence, with a strong emphasis on Generative AI and scalable LLMOps. This is a critical leadership role responsible for translating complex business challenges into high-impact, production-ready AI/ML solutions. You will be instrumental in building a robust AI innovation pipeline, leading a high-performing technical team, and defining the architecture required for secure, ethical, and performant AI systems that drive significant business transformation and efficiency.

Responsibilities & Deliverables

1. Generative AI Strategy & Architecture

  • Establish and Own the Generative AI Stack: Architect and implement a complete Generative AI ecosystem, encompassing fine-tuned Large Language Models (LLMs), domain-specific knowledge graphs, and Retrieval-Augmented Generation (RAG) systems.
  • LLM Development and Optimization: Lead the development, training, and fine-tuning of proprietary LLMs using advanced techniques to ensure relevance and accuracy for business operations.
  • Data Foundation: Design and enforce an enterprise-level data architecture and data governance strategy (clean, complete, and accessible data) essential for fueling advanced AI/ML and analytics.

2. MLOps & Production Scaling

  • Scalable AI Infrastructure: Direct the implementation of LLMOps and robust MLOps practices to achieve seamless, automated deployment, monitoring, and versioning of all AI/ML models across enterprise systems.
  • Performance Engineering: Optimize inference pipelines and AI model efficiency using frameworks like ONNX and DeepSpeed to ensure low-latency, high-throughput performance in production.
  • System Integration: Ensure secure and resilient integration of AI services and APIs with core business applications and existing infrastructure.

3. Team Leadership & Business Impact

  • Technical Leadership: Recruit, mentor, and lead a diverse team of Data Scientists, AI Engineers, and MLOps Professionals, fostering a culture of technical excellence, accountability, and continuous learning.
  • Business Automation: Identify and execute AI-powered automation initiatives that result in measurable improvements in operational efficiency, workflow simplification, and enhanced business decision-making.
  • Ethical AI & Compliance: Define and uphold standards for AI Ethics, bias mitigation, and regulatory compliance in all AI solution designs and deployments.

Required Skills & Qualifications

Educational Background

  • Bachelors or Masters degree in Computer Science, Data Science, Artificial Intelligence, or a closely related quantitative field.

Essential Technical Expertise

  • Extensive experience in leading and shipping enterprise-scale AI/ML products.
  • Deep, demonstrable expertise in Generative AI, LLMs, and RAG systems.
  • Hands-on, production-level experience with MLOps and LLMOps practices (e.g., orchestration, CI/CD for ML, monitoring).
  • Expert proficiency in major AI/ML frameworks: PyTorch, TensorFlow, and optimization frameworks like ONNX or DeepSpeed.
  • Proven experience in NLP, Computer Vision, or time-series AI solutions.
  • Strong background in cloud-native AI development and services (e.g., AWS SageMaker, Azure ML, Google AI Platform) is a significant advantage.

Leadership & Soft Skills

  • Demonstrated ability to communicate complex AI concepts and strategies to both technical teams and non-technical Senior Leadership/Stakeholders.
  • Proven track record of driving tangible business impact through deployed AI solutions.
  • Exceptional team leadership, mentorship, and project management capabilities.

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