Solution Architect

Role Summary

A hands-on technical authority responsible for the end-to-end design, implementation, and governance of enterprise-grade GenAI solutions (LLMs, RAG, Agentic AI) specifically for the Financial Services domain .

Key Responsibilities

• Architectural Leadership: Define blueprints for RAG, AI Agents, and model fine-tuning; evaluate LLMs, vector databases (Pinecone, Weaviate), and frameworks (LangChain, LlamaIndex).
• Hands-on Development: Build production-grade POCs and prototypes; integrate AI with enterprise systems (CRM/ERP) via microservices.
• Governance & Compliance: Ensure solutions meet strict financial regulations (GDPR/HIPAA) and implement Responsible AI (bias mitigation, toxicity filtering).
• LLMOps: Standardize automated deployment, monitoring (drift/hallucination), and CI/CD pipelines for AI.
• Strategic Advisory: Consult with C-level executives on AI roadmaps and mentor technical teams.

Required Qualifications

• Experience: 10+ years in Solution/Data Architecture, with 5+ years specifically in GenAI/LLM production systems.
• Technical Mastery (MUST):
• Deep expertise in RAG, Agentic AI , and LLM fine-tuning.
• Proven deployment of diverse models (GPT, Claude, Llama, Gemini).
• Expertise in Python and frameworks like LangChain or AutoGen .
• Proficiency in at least one major cloud (AWS, Azure, or GCP ).
• Domain Expertise (MUST): Solid background in Financial Services (Banking, Insurance, or FinTech) and its regulatory landscape.
• Tools: Mastery of vectorization, containerization (Docker/Kubernetes), and MLOps tools.