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.