Generative AI Engineer

Locations: Hyderabad / Pune / Chennai / Bangalore / Noida

6+ years of experience in Generative AI, focusing on LLMs, NLP techniques, and

financial applications.

Key Responsibilities:

 Generative AI Model Development: Develop advanced Generative AI models

leveraging LLMs (e.g., GPT,Claude,Gemini,LLama) to automate and enhance

decision-making, report generation, and analysis, specifically within financial

contexts.

 GenAI Ops: Implement GenAI Ops (Generative AI Operations) principles, managing

the AI lifecycle from data operations and model monitoring to maintenance and

optimization. Ensure operational readiness and reliability of AI solutions.

 Human-in-the-Loop (HITL): Establish HITL feedback mechanisms to refine and

validate AI-generated outputs. Collaborate with financial domain experts to improve

model performance and ensure model accuracy, relevance, and alignment with

business objectives.

 Retrieval-Augmented Generation (RAG): Integrate RAG techniques to enhance LLM

performance by enabling the retrieval of up-to-date, authoritative information from

external knowledge sources. This is critical for providing accurate and reliable

insights, especially in financial applications.

 Deployment & Scalability: Lead the deployment of GenAI models in cloud

environments, ensuring scalability, security, and seamless integration with existing

financial systems.

Experience:

 Proficiency in GenAI frameworks like LangChain, Llama Index, Hugging Face, etc.

 Strong understanding of Generative AI deployment strategies, including pilot

programs, technical assessments, and governance planning.

 Expertise in GenAI Ops: managing the lifecycle of Generative AI models, including

model deployment, monitoring, versioning, and optimization.

 Hands-on experience in Retrieval-Augmented Generation (RAG) to connect

generative models to external data sources for improved performance and accuracy.

 Understanding of financial datasets and use cases, including financial reporting, risk

management, and fraud detection.

 Proficiency in Python, with deep knowledge of machine learning frameworks (e.g.,

TensorFlow, PyTorch, scikit-learn, pandas, NumPy).

 Familiarity with cloud-based platforms like AWS, Azure, or Google Cloud for AI

model deployment.

 Knowledge of MLOps,GenAIOps practices, including version control, experiment

tracking, and model monitoring.

 Strong communication skills, with the ability to explain complex AI concepts to non-

technical stakeholders.

 Analytical mindset with a focus on innovation and solving complex financial

problems using AI.