Job Description: Principal Enterprise AI & Solution Architect (BFSI)

Role Overview:

We are seeking a visionary and hands-on Principal Enterprise AI & Solution Architect to design, build, and deploy next-generation intelligent platforms. This role sits at the intersection of robust enterprise engineering and cutting-edge artificial intelligence. You will lead the technical strategy and execution of production-grade Generative AI (GenAI), LLM, and multi-agent systems tailored specifically for the highly regulated Banking, Financial Services, and Insurance (BFSI) sector. As a client-facing technical leader, you will bridge the gap between complex business challenges and scalable, secure AI solutions transforming technical capabilities into high-impact business outcomes.

Key Responsibilities:

1. Solution Architecture & Leadership:

- Architect Production-Grade AI Systems: Design and supervise the deployment of robust end-to-end GenAI architectures, Retrieval-Augmented Generation (RAG) pipelines, and intelligent agent frameworks.

- Client-Facing Strategy: Serve as the primary technical advisor to C-suite and senior stakeholders within financial institutions, articulating complex AI concepts into clear business value propositions.

- Platform Integration: Oversee the architectural fusion of advanced AI technologies with legacy core banking, capital markets infrastructure, and modern data platforms.

2. Engineering & Hands-On Execution:

- Agentic Frameworks & Orchestration: Build, evaluate, and optimize autonomous multi-agent systems using orchestration layers (e.g., LangChain, LangGraph, AutoGen, or LlamaIndex).

- Data & Knowledge Engineering: Architect enterprise RAG pipelines utilizing high-performance vector databases (e.g., Pinecone, Milvus, Qdrant, PGVector) paired with advanced chunking, metadata tagging, and semantic search algorithms.

- Cloud & Infrastructure Lifecycle: Design highly scalable, cloud-native solutions (AWS, Azure, or GCP) that strictly adhere to BFSI standards for low latency, high availability, and extreme security boundaries.

3. Governance, Security & Compliance:

- BFSI Security Blueprinting: Architect systems with zero-trust security profiles, implementing strict data masking, role-based access control (RBAC), and absolute data privacy compliance (GDPR, CCPA, SOC2, HIPAA).

- Guardrails & Evaluation: Implement robust LLM observability, evaluations, and real-time guardrails (e.g., NeMo Guardrails, TruLens) to monitor hallucinations, bias, and jailbreak attempts in highly critical financial environments.

Experience & Qualifications:

Minimum Requirements:

- Core Engineering Foundation: 2+ years of formal experience in infrastructure architecture, core data engineering, or high-throughput platform engineering.

- AI/ML Domain Expertise: 5+ years of dedicated, hands-on experience designing, training, fine-tuning, or productionizing AI/ML and GenAI systems.

- BFSI Domain Fluency: Direct, deep project exposure to at least one core financial sector: Retail/Commercial Banking, Capital Markets, Payments Processing, or Insurance. Must understand industry-specific compliance standards and data structures.

Technical Skill Matrix:

- GenAI / LLMs: Commercial models (OpenAI, Anthropic, Gemini) and Open Source fine-tuning (Llama 3, Mistral), Prompt Engineering, Fine-tuning (LoRA/QLoRA).

- Orchestration & Agents: LangChain, LangGraph, AutoGen, CrewAI, LlamaIndex, Semantic Kernel.

- Vector Infra & Data: Pinecone, Milvus, Qdrant, Chroma, PGVector, Elasticsearch.

- Data & Cloud Platforms: Snowflake, Databricks, Spark, Kafka; AWS/Azure/GCP cloud native services, Docker, Kubernetes (K8s).

- LLM Ops / Observability: LangSmith, Phoenix, Weights & Biases, MLflow, Guardrails frameworks.