Gen AI Architect

Gen AI Solution Architect Position SummaryThe Principal Generative AI Solution Architect will serve as the principal hands-on technical authority responsible for the end-to-end design, implementation, and governance of all enterprise-grade Generative AI (GenAI) solutions. 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, especially within the Financial Services domain, into secure, scalable, and ethically compliant GenAI applications that drive measurable organizational value.This role is expected to actively build GenAI applications, develop Proofs-of-Concept (POCs), and deliver production-grade GenAI solutions to solve critical business problems.Key Responsibilities and DutiesArchitectural Strategy and Hands-On DesignDefine GenAI Architecture: Establish the architectural blueprint, reference architectures, and technology standards for deploying GenAI solutions, including Retrieval-Augmented Generation (RAG), AI Agents, Agentic AI autonomous agents, and model fine-tuning pipelines.Hands-On Development and POCs (MUST): Design, build, and deploy high-impact GenAI solutions, including developing hands-on prototypes and POCs to validate technical designs and business value. Hands-on on RAG), AI Agents, Agentic AI, frameworks and SDKsTechnology Selection and Evaluation: Conduct rigorous evaluation, benchmarking, and selection of foundational models (MUST have worked on all leading LLM models), vector databases (e.g., Pinecone, Weaviate), and orchestration frameworks (e.g., LangChain, LlamaIndex, Agentic AI frameworks).Cloud Deployment: Architect, implement, and deploy GenAI solutions on at least one of the major cloud providers (AWS, Azure, or GCP).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 ComplianceFinancial Services Compliance: Ensure all GenAI architectures comply with rigorous regulations, audit standards, and data privacy mandates specific to the Financial Services industry (e.g., GDPR, HIPAA, or industry-specific compliance standards).Responsible AI and Governance: Operationalize and enforce enterprise-wide Responsible AI policies, including mechanisms for bias mitigation, toxicity filtering, data provenance, and explainability (XAI).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 LeadershipTechnical Advisory and Use Case Definition: Serve as the Generative AI Subject Matter Expert (SME) in engagements with C-level executives 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 ExperienceTechnical ExpertiseExperience: Minimum of 10 years of experience in Solution Architecture, Data Architecture, or ML Engineering, with a minimum of 5 years dedicated to architecting and building production-grade Generative AI or Large Language Model solutions.Generative AI: Deep, hands-on expertise with LLMs, Transformer architectures, Fine-Tuning/Transfer Learning, and complex techniques like RAG and advanced Prompt Engineering.LLM Model Exposure (MUST): Proven experience and deployment of a diverse range of foundational models (including both commercial and open-source, e.g., GPT, Claude, Llama, Gemini).Agentic AI Frameworks (MUST): Hands-on experience designing and implementing Agentic AI systems using modern frameworks (e.g., LangChain, LlamaIndex, AutoGen).Cloud Platforms (MUST): 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.DevOps/MLOps: Strong understanding of containerization (Docker, Kubernetes) and MLOps/LLMOps tools for managing the lifecycle of production AI models.Professional & EducationDomain Expertise (MUST): Proven background and experience working in the Financial Services domain (Banking, Insurance, Capital Markets, FinTech), with deep knowledge of industry-specific data, challenges, and regulatory environments.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, C-level audiences.Certifications (Preferred): Relevant certifications such as AWS/Azure/GCP Solution Architect Professional, or specialized AI/ML certifications.