AI Enterprise Architect

Regional AI Technical Lead is a strategic technical leader responsible for designing, implementing, and managing advanced generative AI solutions across Google Cloud Platform (GCP), Microsoft Azure, and Amazon Web Services (AWS). This role combines deep expertise in generative AI technologies, multi-cloud architecture, and modern software engineering practices to deliver scalable, secure, and innovative AI solutions. The architect leads cross-functional teams, drives technical vision, and ensures alignment between business objectives and AI initiatives. Strategy & Roadmap

• Define and drive the AI strategy, aligning with business goals and innovation priorities.
• Develop and maintain the AI solution roadmap, including short-term deliverables and long-term vision.
• Evaluate emerging AI trends and technologies to inform strategic direction.

Architecture & Design

• Architect end-to-end AI solutions using Gen AI, Agentic AI, LLMs, and multi-modal AI.
• Design intelligent agent systems using LangChain, LangGraph, Model Context Protocol (MCP), Agent to Agent Protocols, Google AI Development Kit (ADK), Azure AI Studio or AWS Bedrock.
• Integrate large language models (LLMs) such as Llama, Gemini, GPT, and Claude into enterprise systems and custom applications.
• Establish scalable and modular AI architectures that support RAG pipelines, Vector DBs (ChromaDB, Pinecone, FAISS, Weaviate, Vertex AI Matching Engine).
• Develop and optimize retrieval-augmented generation (RAG) pipelines using vector databases
• Define and enforce AI governance frameworks, including Responsible AI, GuardRails, and compliance w ith AI Ethics & Regulations.
• Supervise the design, fine-tuning, and optimization of generative AI models and multimodal systems (text, image, audio).
• Lead Python-based development for prompt orchestration, tool agents, APIs, and data pipelines.

Assessment & Optimization

• Conduct technical assessments of existing AI/ML systems, models, and data pipelines.
• Identify gaps, risks, and opportunities for modernization or enhancement.
• Recommend architectural improvements and integration strategies for legacy systems.

Cloud Platform Integration & Management

• Architect, deploy, and monitor generative AI solutions on GCP (Vertex AI, Document AI, AlloyDB, BigQuery, Cloud Run), Azure (OpenAI Service, Cognitive Search, Azure ML, Azure Functions), and AWS (Bedrock, SageMaker, Lambda, API Gateway, DynamoDB).
• Design and manage scalable cloud infrastructure, ensuring performance, cost efficiency, and compliance across platforms.
• Implement containerization and orchestration strategies using Docker and Kubernetes (GKE/EKS/AKS) for reliable deployment.
• Establish and enforce security frameworks using GCP IAM, Azure Identity, AWS IAM, and related tools for secure, compliant access.
• Utilize monitoring and logging solutions such as Google Cloud Operations Suite, Azure Monitor, and AWS CloudWatch.

MLOps, DevOps & Governance

• Automate model deployment, versioning, and monitoring using MLOps/DevOps best practices and CI/CD pipelines.
• Implement prompt optimization, context management, and model performance tuning.
• Ensure adherence to data governance, privacy, PII handling, and AI ethics principles throughout the development lifecycle.

Leadership & Collaboration

• Collaborate with product owners, data scientists, engineers, and business stakeholders.
• Mentor engineering teams and contribute to talent development in AI and ML domains.
• Represent AI architecture in enterprise governance forums and technical councils.

• Generative AI (Gen AI), Agentic AI
• Model selection, evaluation, interpretability: TensorFlow, PyTorch, Hugging Face, NLP, computer vision, time-series modeling.
• AI Strategy, Architecture, and Roadmap Planning
• Python / R / TypeScript Programming
• AI Frameworks (LangChain, AutoGen, Azure AI Foundry, Azure AI Agent, CrewAI, LangGraph, Google ADK)
• Model Context Protocol (MCP), Agent to Agent Protocol
• N-8-N
• GuardRails, AI Ethics and Regulations
• Prompt Engineering: “Expertise in prompt engineering, LLM operations, and GenAI deployment best practices.”
• Distillation, RAG, Fine-tuning
• Multi-modal AI, LLMs
• Vector Databases, Embeddings
• GenAI deployment tools (Docker, Kubernetes)
• AI Solution Assessment and Optimization
• ETL, Data Pipelines, Feature Stores: Experience with tools like Airflow, dbt, MLflow, DVC, Azure ML pipelines.
• CI/CD for ML: Automated model retraining, versioning, and monitoring.
• Data anonymization, privacy-by-design, secure model deployment: Especially for regulated industries (GDPR, SOX, HIPAA).
• Desirable certifications: AI/ML, cloud architecture, relevant technology (e.g., GCP GenAI Leader, AWS AI Practitioner, Azure AI certifications), Job Category: Pre-Sales Degree Level: Bachelor’s degree Job Description: The Nokia Client Chief Technology Officer (CTO) is a senior, customer-facing technology leader respon…
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