- Define GenAI Architecture : Establish the architectural blueprint, reference architectures, and technology standards for deploying GenAI solutions, including Retrieval-Augmented Generation (RAG), autonomous agents, and model fine-tuning pipelines.

- Technology Selection and Evaluation : Conduct rigorous evaluation, benchmarking, and selection of foundational models (both commercial and open-source, e.g., GPT, Claude, Llama), vector databases (e.g., Pinecone, Weaviate), and orchestration frameworks (e.g., LangChain, LlamaIndex).

- Integration Planning : Design robust integration patterns (APIs, microservices, event-driven architectures) to seamlessly connect GenAI capabilities with core enterprise platforms (CRM, ERP, HRIS) and existing data infrastructure.

- Performance and Cost Optimization : Architect solutions with a focus on high-throughput, low-latency inference, and optimization of computational resources (GPU/TPU utilization) to ensure cost-efficiency at enterprise scale.

Governance, Security, and Compliance :

- Responsible AI and Governance : Operationalize and enforce enterprise-wide Responsible AI policies, including mechanisms for bias mitigation, toxicity filtering, data provenance, and explainability (XAI) within all GenAI deployments.

- Data Security and Privacy : Design data workflows and security measures to ensure sensitive enterprise and customer data is protected throughout the GenAI lifecycle, adhering to regulations such as GDPR, HIPAA, and industry-specific compliance standards.

- LLMOps Implementation : Define and standardize LLMOps practices, including automated model deployment, continuous monitoring for model drift and hallucination, version control, and CI/CD pipelines for AI assets.

Stakeholder Engagement and Leadership :

- Technical Advisory : Serve as the Generative AI Subject Matter Expert (SME) in engagements with C-level executives, product owners, and business unit leaders to define high-impact use cases and communicate technical risks and trade-offs.

- Mentorship and Enablement : Provide technical leadership, guidance, and mentorship to Data Science, ML Engineering, and Software Development teams on best practices for GenAI architecture, prompt engineering, and secure coding.

- Innovation Roadmap : Develop and maintain a forward-looking Generative AI technology roadmap, constantly evaluating emerging trends (e.g., multi-modal models, agentic frameworks) and proposing pilots and strategic investments.

Required Qualifications and Experience :

Technical Expertise :

- Experience : Minimum of 10 years of experience in Solution Architecture, Data Architecture, or ML Engineering, with a minimum of 3 years dedicated to architecting production-grade Generative AI or Large Language Model solutions.

- Generative AI : Deep, hands-on expertise with LLMs, Transformer architectures, FineTuning/Transfer Learning, and complex techniques like RAG and advanced Prompt Engineering.

- Cloud Platforms : Expert-level proficiency with a major cloud provider (AWS, Azure, or GCP) and their respective AI/ML service offerings (e.g., Amazon Bedrock, Azure OpenAI Service, Google Vertex AI).

- Programming : Mastery of Python, including relevant data science and ML libraries (PyTorch, TensorFlow).

- Data Systems : Proven experience designing data pipelines for GenAI, including vectorization, embedding models, and integration with modern data architectures (data lakes, data meshes).

- DevOps/MLOps : Strong understanding of containerization (Docker, Kubernetes) and MLOps tools for managing the lifecycle of production AI models.

Professional & Education :

- Education : Bachelor's or Master's degree in Computer Science, Data Science, Engineering, or a related quantitative field.

- Communication : Exceptional written and verbal communication skills, with the ability to create clear architectural documentation and present complex technical strategies to both technical and non-technical audiences.

- Certifications (Preferred) : Relevant certifications such as AWS/Azure/GCP Solution Architect Professional, or specialized AI/ML certifications.