AI Engineer

AI Engineer

We are seeking a dynamic AI Engineer to join our pioneering Agentic AI team. The ideal candidate will possess a strong foundation in delivering projects on both SaaS & PaaS based platforms, Prompt Engineering, UI development and development of APIs. You will be involved in the end-to-end stage of the development life cycle.

Duties and Responsibilities

Deliveries with respect to Agentic AI Platform:

• Build and maintain autonomous software agents using state-of-the-art LLM frameworks.
• Collaborate with product owners and domain experts to build reusable components for business process automation.
• Develop core infrastructure and reusable components to support the deployment of agent-based AI systems.
• Work on agent orchestration, prompt engineering, and LLM-powered integrations.
• Implement scalable solutions integrated with CRM systems and enterprise data platforms.
• Contribute to the design of modular, extensible, enterprise-grade architecture.
• Fine-tune and evaluate AI agents for speed, accuracy, performance and maintainability across business units.
• Contribute to CI/CD automation and maintain operational stability of agent services.

Generative AI & Model Optimization:

• Writing and Optimizing Prompts
• 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 agentic and autonomous actions.
• Selecting appropriate model so that Agents can complete the autonomous tasks in an efficient manner.
• Defining reusable components in the platform.
• Delivery using configuration approach should be the first preference; in case that does not work, customization should be pursued.
• Defining configuration parameters and incorporating them as platform design.
• Testing and end-to-end testing of the project deliverable.
• Load balancing between different models.
• Always have a 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: Analyzing 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.

Major Challenges

• Support from other platform owners.
• Agents must learn from failed interactions.
• Building an Agent 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 agentic application.

Required Qualifications and Experience

• Qualifications: Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.
• Experience: 2–4 years of experience in Data Technologies like Data Lake, Fabric, Data bricks etc.
• Work Experience: Strong understanding of prompt engineering, tool calling, and agent orchestration.
• Good understanding of Data Platforms Like Data Lake, Data Bricks & MS Fabric.
• Must have good understanding of Microsoft Fabric Data Agents.
• Understanding of Microsoft Copilot Studio, Power Automate, and Power BI.
• Familiarity with the Agent Development Kit (ADK) and protocols like MCP & A2A.
• Familiarity with LangChain, Semantic Kernel, CrewAI, or LangGraph.

Key skills/competency

• AI Engineer
• Agentic AI
• LLM Frameworks
• Prompt Engineering
• Generative AI
• Model Optimization
• Self-Learning Systems
• Data Platforms
• Microsoft Fabric
• LangChain