About the Role :

Join the high-impact AI Engineering team of a leading global financial services enterprise. In this hands-on, product-facing role, you will focus on designing and building advanced Retrieval-Augmented Generation (RAG) systems that enable seamless, accurate querying across massive internal document estates. You will play a pivotal role in delivering generative AI solutions and will gain early exposure to cutting-edge, agent-based architectures.

Key Responsibilities :

- LLM &
- GenAI Development : Build production-grade applications leveraging state-of-the-art models (GPT, Llama, Gemini, Claude); optimize prompt engineering, embeddings, and inference workflows.
- RAG Pipeline Architecture : Design, implement, and maintain high-performing retrieval pipelines, including document chunking, vector search, and response relevance evaluation.
- Agentic Systems : Develop agent coordination, tool integration, and orchestration logic using frameworks such as LangChain or Google ADK.
- Core NLP &
- Data Pipelines : Build robust NLP pipelines, handle data preprocessing, and establish rigorous model evaluation metrics.
- Production &
- Deployment : Develop reusable, scalable AI services and REST APIs,



containerizing applications using Docker for cloud deployment.

Requirements &

Qualifications :

Education : B.Tech / B.E. in Computer Science, Data Science, or a related field.

Experience :

- 5 years of total software engineering experience.
- 3 years specifically in Machine Learning and Natural Language Processing (NLP).
- 1.5 years of hands-on experience building production LLM applications.

Technical Skills :

- Python : Production-grade development with solid coding best practices.
- Vector Databases : Direct experience with at least one vector database/search engine (e.g., Elasticsearch, OpenSearch, FAISS).
- NLP Foundations : Solid grounding in tokenization, embedding techniques, and transformer architectures.
- DevOps &
- Cloud : Hands-on experience with Docker, microservices architecture, and deployment across major cloud platforms (AWS, Azure, GCP, or OCP).

Nice-to-Have :

- Practical experience with agent workflows and tool-based reasoning paradigms.
- Familiarity with deep learning frameworks (PyTorch, TensorFlow) and MLOps tooling.
- Container orchestration experience with Kubernetes.
- Prior domain exposure to Financial Services or BFSI environments.

📌 AI Engineer - LLM/RAG (India)
🏢 Talent Socio
📍 India