Agentic AI Engineer Co-Op

Responsibilities

• check_circle Design & Develop Agentic Systems: Build intelligent agents capable of autonomous planning, reasoning, and task execution, often using LLMs (e.g., GPT-class, LLaMA), multi-modal models, and autonomous workflows
• check_circle Orchestration & Frameworks: Implement agent orchestration using frameworks like LangChain, AutoGen, CrewAI, Semantic Kernel, or custom solutions
• check_circle Retrieval-Augmented Generation (RAG): Design and optimize RAG pipelines for enhanced reasoning with external knowledge, including document ingestion, chunking, embeddings, vector stores, and retrieval ranking
• check_circle Tool & Memory Integration: Develop agents that call APIs, databases, and other tools, maintain memory, and adapt based on outcomes
• check_circle Evaluation & Monitoring: Create evaluation frameworks for accuracy, grounding, latency, and cost; build observability for agent behavior and failure modes
• check_circle Model Adaptation: Fine-tune or adapt foundation models (e.g., via LoRA, adapters) for domain-specific use cases
• check_circle Production Deployment: Deploy GenAI/agentic systems in cloud-native environments with CI/CD, versioning, and runtime safeguards
• check_circle Cross-Functional Collaboration: Work with data scientists, ML engineers, product teams, and governance/compliance stakeholders

Basic qualifications

• 2+ years in AI/ML system design, deployment, or autonomous agent development
• Programming: Proficiency in Python (and sometimes Java, C#) for AI/ML solution development
• Agent & Workflow Expertise: Experience with agent orchestration frameworks and multi-agent communication protocols
• RAG & LLM Integration: Hands-on with RAG architectures, evaluation methodologies, and LLM integration
• Cloud & DevOps: Experience with cloud platforms (e.g., Azure, AWS) and CI/CD pipelines
• Governance & Compliance: Understanding of responsible AI, security, and compliance in regulated domains (e.g., retail)

About the company