Role Overview

We are looking for a Generative AI Engineer to spearhead the integration of Large Language Models (LLMs) into our financial ecosystem. Your mission is to build intelligent systemsfrom automated financial advisory bots to complex document analysis tools
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that are secure, explainable, and highly accurate. You will move beyond simple prompting to build production-ready RAG pipelines and fine-tuned models that handle sensitive financial data.

Key Responsibilities :

- LLM System Architecture : Design and implement robust Retrieval-Augmented Generation (RAG) systems to ground LLM outputs in our proprietary financial data, reducing hallucinations.

- Agentic Workflows : Build AI agents capable of executing multi-step financial tasks, such as automated credit risk assessment, fraud pattern detection, or generating personalized investment reports.

- Model Optimization : Fine-tune open-source models (e.g., Llama, Mistral, or Falcon) using PEFT (LoRA/QLoRA) for domain-specific financial terminology and compliance tasks.

- Guardrails & Compliance : Implement rigorous "AI Guardrails" to ensure model outputs comply with financial regulations (e.g., GDPR, SEC, or SOC2) and prevent the leakage of PII (Personally Identifiable Information).

- Evaluation Frameworks : Develop custom evaluation metrics (using tools like RAGAS or TruLens) to measure the faithfulness, relevance, and accuracy of AI responses in a financial context.

- Production Deployment : Deploy and scale LLM applications using modern stacks, ensuring low latency for real-time customer interactions.

Required Skills & Qualifications :

- Generative AI Stack : Deep experience with LangChain, LlamaIndex, or Haystack.

- LLM Infrastructure : Proficiency in working with model providers (OpenAI, Anthropic, Bedrock) and self-hosting models via vLLM or TGI.

- Vector Databases : Expertise in indexing and querying high-dimensional data using Pinecone, Weaviate, or Qdrant.

- Python Mastery : High proficiency in Python, including asynchronous programming (FastAPI) for handling high-volume financial transactions.

- Data Security : Understanding of encryption, data masking, and secure multi-party computation in the context of AI.

- Education : B.S./M.S. in Computer Science, AI, or a related field; a background in Finance or Quantitative Analysis is a significant plus.

Preferred Attributes :

- Experience building LLM-based fraud detection or automated compliance monitoring systems.

- Familiarity with financial data formats (FIX, SWIFT, or XBRL).

- A "Safety-First" mindset regarding AI ethics and bias mitigation in lending or credit scoring.