- Define GenAI Architecture : Establish the architectural blueprint, reference architectures, and technology standards for deploying GenAI solution including Retrieval-Augmented Generation (RAG), autonomous agents, and model fine-tuning pipelines. Complete Hands-on in developing Agentic AI applications for production scalable experience is mandatory.

- 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., OpenSearch,Pinecone, Weaviate), and orchestration frameworks (e.g., LangChain, LlamaIndex, openAISDK).

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

- 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.

- 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.

- 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. 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.

Required Qualifications and Experience :

Technical Expertise :

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

- Generative AI : Deep, hands-on expertise with LLMs, Transformer architectures, Fine-Tuning/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. Ability to work in a dynamic and high-pressure environment with a solution mind-set.

Professional Attributes :

- Education : Bachelors or Masters 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 nontechnical audiences.

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