Job Overview :

We are seeking an experienced Generative AI Specialist with a strong focus on Large Language Models (LLMs), NLP techniques, and financial applications.

The ideal candidate will lead the development, deployment, and optimization of Generative AI solutions to enhance decision-making, reporting, and analytical capabilities within financial domains.

Key Responsibilities :

Generative AI Model Development :

- Design, develop, and optimize advanced Generative AI solutions using LLMs such as GPT, Claude, Gemini, LLaMA, etc.

- Apply GenAI to automate financial decision-making, report generation, insights extraction, and analytical workflows.

GenAI Ops (Generative AI Operations) :

- Implement GenAI Ops best practices to manage the end-to-end AI lifecycle, including data operations, model monitoring, maintenance, and optimization.

- Ensure high availability, reliability, and performance of deployed GenAI solutions.

Human-in-the-Loop (HITL) :

- Establish HITL frameworks to validate, refine, and improve AI-generated outputs.

- Collaborate closely with financial domain experts to enhance accuracy, relevance, and business alignment of models.

Retrieval-Augmented Generation (RAG) :

- Architect and implement RAG pipelines to integrate external, authoritative knowledge sources.

- Ensure accurate, up-to-date, and compliant AI-generated insights, particularly for financial use cases.

Deployment & Scalability :

- Lead deployment of GenAI solutions on cloud platforms, ensuring scalability, security, and seamless integration with existing financial systems and workflows.

Required Skills & Experience :

- 6+ years of hands-on experience in Generative AI, LLMs, and NLP, preferably within financial applications.

- Strong experience with GenAI frameworks and tools such as LangChain, LlamaIndex, Hugging Face, etc.

- Expertise in GenAI Ops / MLOps, including model deployment, monitoring, versioning, optimization, and governance.

- Hands-on experience implementing Retrieval-Augmented Generation (RAG) architectures.

- Solid understanding of financial datasets and use cases such as financial reporting, risk management, fraud detection, and analytics.

- Proficiency in Python and ML frameworks including TensorFlow, PyTorch, scikit-learn, along with pandas and NumPy.

- Experience deploying AI solutions on AWS, Azure, or Google Cloud Platform.

- Familiarity with experiment tracking, version control, and model monitoring tools.

- Strong communication skills with the ability to translate complex AI concepts to non-technical stakeholders.

- Analytical, innovative mindset with a passion for solving complex financial problems using AI