Vendor Personnel

1. Design & Development of AI Agents

• Design, build, and deploy intelligent autonomous AI agents leveraging LLMs, memory frameworks, and multi-agent coordination systems.
• Develop agentic workflows using frameworks such as LangChain, CrewAI, AutoGen, and LlamaIndex.
• Implement reasoning, planning, and decision-making capabilities within agent systems.

2. Multi-Agent Systems & Orchestration

• Design and manage multi-agent architectures enabling collaboration, task delegation, and role-based behaviors.
• Optimize agent communication, tool usage, and execution flow for complex tasks.

3. API & Tool Integration

• Integrate AI agents with third-party APIs, internal systems, databases, and external tools.
• Enable real-time data access, tool calling, and dynamic task execution.

4. Performance Optimization & Intelligence Enhancement

• Apply Reinforcement Learning (RL) techniques to improve agent decision-making and efficiency.
• Implement Retrieval-Augmented Generation (RAG) pipelines using vector databases.
• Optimize prompt engineering, context management, and memory strategies for accuracy and scalability.

5. End-to-End AI Pipelines

• Build scalable, production-ready pipelines for agent deployment, monitoring, logging, and continuous improvement.
• Ensure reliability, observability, and performance in real-world environments.

6. Collaboration & Stakeholder Engagement

• Work closely with product managers, ML engineers, and domain experts to translate business requirements into functional agentic AI solutions.
• Participate in design reviews, architecture discussions, and solution planning.

Required Qualifications

Education

• Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Data Science, or a related field.

Experience

• 5+ years of experience in software development and/or AI/ML engineering.
• Hands-on experience working with Large Language Models (LLMs) in production environments.

Technical Skills

• Strong proficiency in Python.
• Experience with agentic AI frameworks such as LangChain, LlamaIndex, AutoGen, CrewAI.
• Hands-on experience with LLM APIs (OpenAI, Anthropic, Google Gemini, or similar).
• Experience with vector databases like Pinecone, Chroma, FAISS.
• Solid understanding of prompt engineering, agent orchestration, and context/memory management.
• Familiarity with RAG architectures and knowledge retrieval techniques.
• Experience working in cloud environments (AWS, GCP, Azure).
• Knowledge of containerization and orchestration tools such as Docker and Kubernetes.

Preferred / Good-to-Have Skills

• Experience with reinforcement learning for agent optimization.
• Exposure to monitoring and evaluation of LLM-based systems.
• Understanding of security, scalability, and cost optimization for AI systems.
• Experience deploying AI solutions in enterprise or client-facing environments.