AI Agent & Automation Developer (Software Engineer)

Role & responsibilities

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. You must be adaptable, collaborative, and eager to learn new technologies to address multifaceted business problems.

A successful candidate is a proactive problem-solver with strong software engineering skills, ready to thrive in a fast-paced environment and communicate effectively with both technical and business stakeholders.

• 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. This includes applying methods such as:
• Prompt Engineering injecting information directly into prompts.
• Fine-tuning training models on additional data to embed knowledge (where applicable).
• Retrieval-Augmented Generation (RAG) embedding documents into vector databases and retrieving semantically relevant information.
• Tool Use leveraging sub-agents or deterministic models to locate and add relevant context.
• Hybrid Approaches – combining two or more methods to maximize reliability and relevance.
• Monitor performance, conduct evaluations, and implement safety and guardrail mechanisms.
• Maintain thorough documentation of agent logic, design decisions, and dependencies.
• Stay up to date with industry developments and advancements in AI, machine learning, and agent-based technologies.
• Collaborate with stakeholders and engineering teams to integrate AI agents with Microsoft-specific platforms such as Copilot Studio, Azure AI, and Semantic Kernel SDK.

Build and deploy enterprise-grade Copilots using Microsoft’s orchestration tools, enabling seamless interaction across Microsoft 365, Dynamics, and custom business applications

Preferred Skills-

• Bachelor’s degree in Computer Science, AI, Machine Learning, or a related field.
• Proven experience in agent-based systems, or intelligent automation, with a minimum of 4-5 years in a similar role. Overall 5+ years of experience
• Strong programming skills in Python (or similar) and experience with AI/ML libraries (e.g., LangChain, OpenAI API, Hugging Face, PyTorch or similar).
• Understanding of agent-based modeling, multi-agent systems, or reinforcement learning.
• Experience in building RAG based models
• Experience building applications with LLMs or generative AI models and various protocols such as MCP, A2A etc.
• Familiarity with API development, backend services, and deployment pipelines.
• Ability to work independently in experimental and fast-paced environments.
• Excellent communication and interpersonal skills, with the ability to build strong relationships with business and technical stakeholders.
• Analytical mindset with the ability to solve complex problems and make data-driven decisions.
• Demonstrated understanding of data quality, integrity, and systems integration

Understands AI governance, building observable and auditable models