Summary :

The ideal candidate will have hands-on experience building autonomous AI agents using large language models (LLMs), agent frameworks, and modern AI infrastructure.

As an AI Agent & Automation Developer, you will design, implement, and deploy intelligent agents that operate autonomously within digital environments, driving innovation and operational excellence.

This role supports organizational transformation by solving complex business challenges through intelligent automation, agent orchestration, and advanced analytics.

Duties and Responsibilities :

- Design and implement AI agents using LLMs, planning algorithms, and decision-making frameworks.

- Develop agent architectures that support autonomy, interactivity, and task completion.

- Integrate agents into applications, APIs, or workflows (e.g., chatbots, automation tools).

- Collaborate with researchers, engineers, and product teams to iterate on agent capabilities.

- Optimize agent behavior through feedback loops, reinforcement learning, or user interaction.

- Engineer and manage context for agents to ensure they access the right information at the right time.

- Monitor performance, conduct evaluations, and implement safety and guardrail mechanisms.
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- Maintain thorough documentation of agent logic, design decisions, and dependencies.

- Collaborate with stakeholders and engineering teams to integrate AI agents with Microsoft-specific platforms such as Copilot Studio, Azure AI, and Semantic Kernel SDK.

Tech Stack & Methods :

- Prompt Engineering, Fine-tuning, Retrieval-Augmented Generation (RAG), Tool Use, Hybrid Approaches.

- Python, LangChain, OpenAI API, Hugging Face, PyTorch.

- API development, backend services, and deployment pipelines.

Requirements :

- Bachelor's degree in computer science, AI, Machine Learning, or a related field.

- 5+ years of overall experience, with 3-5 years in agent-based systems or intelligent automation.

- Strong programming skills in Python and experience with AI/ML libraries.

- Understanding of agent-based modeling, multi-agent systems, or reinforcement learning.

- Experience in building RAG based models and LLM applications.

- Excellent communication and interpersonal skills.

- Understanding of AI governance, building observable and auditable models.