The Generative AI Technical Architect will serve as the principal technical authority responsible for the end-to-end design, implementation, and governance of all enterprise-grade Generative AI (GenAI).

This role requires a blend of deep technical expertise in large language models (LLMs), enterprise system architecture, and strategic acumen to translate complex business objectives into secure, scalable, and ethically compliant GenAI applications that drive measurable organizational value.

Key Responsibilities and Duties :

- 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 : 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 Technical Architect Professional, or specialized AI/ML certifications.