Job description

Job Purpose	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 end-to-end stage of 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 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.
Major Challenges	Support from other platform owners · Agents must learn from failed interactions · Building a Agents 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	a) 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. b) 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.

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Someone shared one job with you. ASAI keeps finding the rest.

Scored, not searched. Every role ranked against your actual profile.

Alerts as often as hourly. Reach new roles while the pile is still small.

Skill gaps, spelled out. See exactly which requirements you don't meet yet.

Verified jobs, only. Say no to ghost jobs. Your time deserves respect.

Job description

Job Purpose	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 end-to-end stage of 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 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.
Major Challenges	Support from other platform owners · Agents must learn from failed interactions · Building a Agents 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	a) 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. b) 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.