Gen AI Architect

Gen AI Solution Architect Position Summary The 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 Duties Architectural Strategy and Hands-On Design Define 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 SDKs Technology 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 Compliance Financial 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 Leadership Technical 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 Experience Technical Expertise Experience:

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 & Education Domain 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.