| Job Purpose | We are seeking a dynamic AI Engineer to join our pioneering Conversational Text AI team. The ideal candidate will possess a strong foundation in model development, UI development and development of APIs, multi-system integrations. You will be involved in end-to-end stage of development life cycle. |
| Duties and Responsibilities | Deliveries with respect to Conversational Text AI Platform: • Build and maintain Conversational Text AI system using state-of-the-art LLM frameworks. • Collaborate with product owners and domain experts to build reusable components for business process. • Develop core infrastructure and reusable components to support the deployment of conversational AI systems. • Work on orchestration, prompt engineering, and LLM-powered integrations. • Implement scalable solutions integrated with respective echo systems and enterprise data platforms. • Contribute to the design of modular, extensible, enterprise-grade architectures. • Fine-tune & optimization for speed, accuracy, performance and maintainability across business units. • Contribute to CI/CD automation and maintain operational stability of application services. Generative AI & Model Optimization: • Fine-tune LLMs/SLMs with proprietary NBFC data. • Perform distillation, quantization of LLMs for edge deployment. • Evaluate and run LLM/SLM models on local/edge server machines. Self-Learning Frameworks: • Build self-learning systems that adapt without full retraining (e.g., learn new rejection patterns from calls). • Implement lightweight local models to enable real-time learning on the edge. |
| Key Decisions / Dimensions | Platform Design & Delivery, Model Selection, Customization (if any) & Testing: • Choosing the right model for various task and actions. • Selecting appropriate model so that tasks can complete in efficient manner. • Defining reusable components in the platform. Delivery using configuration approach should be first preference, in case that does not work should go for customization. • Defining configuration parameters and incorporate them as platform design. • Testing and end to end testing of the project deliverable. • Load balancing between different models. • Always have switch on/switch off feature. • Must have all services backed up on primary/HA & DR servers. Prompt Engineering: • Prompt Design & Development: Crafting prompts that guide AI systems to produce desired outputs for various applications, such as text generation, translation, question answering, and creative writing. • Testing and Evaluation: Analysing the effectiveness of prompts and refining them based on results to ensure accurate and relevant responses. • Bias Mitigation: Designing prompts that minimize bias and ensure fair and equitable outcomes from AI systems.|As per business AOP. |
| Major Challenges | • Support from other platform owners • Building a Conversational Text AI BOT that doesn't just answer but negotiates with human-like reasoning. • Running large AI models in low-latency, low-bandwidth environments without cloud dependency. • Getting the end-to-end domain knowledge • Managing data and information security of the application. |
| Required Qualifications and Experience | Educational Qualifications • Educational Background: Bachelor’s or Master’s degree in computer science, Engineering, or a related field. • Experience: 4–7 years of experience in AI/ML, with exposure to Python, Node.JS, JavaScript, HTML/CSS, Redis, Postgres, Azure COSMOS, DevOps, CI/CD. • Strong programming skills in languages such as Python, Node.JS, JavaScript, HTML/CSS. • Familiarity with Redis, Postgres, Vector Embeddings, Speech-to-Text & Text-to-Speech Services, Azure COSMOS, DevOps, CI/CD, Lang-Chain or Lang-Graph. • Experience building with or integrating LLMs for task automation, reasoning, or autonomous workflows • Strong understanding of prompt engineering, tool calling, and agent orchestration. |