AI Engineer

AI Engineer – Prompt Engineering & Multi-Agent Systems

Corner Stone Technology Talent Services is seeking an experienced AI Engineer to design, build, deploy, and optimize production-ready generative AI solutions. This is a hands-on engineering role for someone with deep experience in prompt engineering, AI agent development, and multi-agent orchestration. The ideal candidate has successfully moved agentic AI solutions beyond proof-of-concept environments and into scalable, reliable deployments. You will develop intelligent agents that can reason, use tools, retrieve enterprise knowledge, coordinate with other agents, and complete complex workflows with appropriate observability, security, and human oversight.

This opportunity is best suited for an engineer who is comfortable owning the full AI solution lifecycle—from prompt and architecture design through deployment, evaluation, monitoring, and continuous improvement.

• Design, develop, and deploy production-grade AI agents and multi-agent systems.
• Create advanced prompts, system instructions, reusable prompt templates, and structured workflows for large language model applications.
• Develop agents capable of planning, reasoning, tool use, function calling, task delegation, and collaboration with other specialized agents.
• Design agent orchestration patterns, including supervisor-agent, planner-executor, routing, sequential, and parallel workflows.
• Integrate AI agents with enterprise applications, APIs, databases, vector stores, document repositories, and external tools.
• Build retrieval-augmented generation solutions that provide relevant, grounded, and context-aware responses.
• Implement short-term and long-term memory strategies for stateful agent interactions.
• Evaluate model and agent performance using measurable criteria such as accuracy, task completion, response quality, latency, cost, and groundedness.
• Identify and reduce hallucinations, prompt injection risks, unreliable tool execution, and unintended agent behavior.
• Implement guardrails, fallback logic, human-in-the-loop approvals, access controls, and responsible AI practices.
• Build monitoring and observability capabilities for prompts, model responses, agent decisions, tool calls, failures, token consumption, and operational costs.
• Troubleshoot production issues involving prompts, agent workflows, integrations, retrieval quality, and model performance.
• Collaborate with software engineers, data engineers, architects, product leaders, and business stakeholders to translate use cases into scalable AI solutions.
• Document solution architecture, agent behavior, prompt versions, evaluation results, deployment procedures, and operational support requirements.

• Approximately seven or more years of overall software engineering, data engineering, machine learning, or artificial intelligence experience.
• At least three years of relevant hands-on experience developing generative AI, prompt engineering, or AI agent solutions.
• Demonstrated experience building and running multiple AI agents in deployed environments.
• Advanced prompt engineering experience, including system prompts, few-shot prompting, chain-of-thought alternatives, structured output, context management, and prompt optimization.
• Strong understanding of agentic AI concepts such as planning, reasoning, memory, tool use, task routing, agent communication, and workflow orchestration.
• Hands-on experience with large language models and generative AI APIs.
• Strong programming experience with Python and modern API development.
• Experience integrating AI applications with REST APIs, databases, cloud services, and enterprise systems.
• Experience with one or more agent or orchestration frameworks, such as Lang Chain, Lang Graph, Microsoft Auto Gen, Semantic Kernel, CrewAI, Llama Index, or comparable technologies.
• Experience implementing retrieval-augmented generation using embeddings, vector search, document chunking, retrieval strategies, and grounding techniques.
• Experience deploying AI services in a cloud environment such as Microsoft Azure, Amazon Web Services, or Google Cloud Platform.
• Understanding of model evaluation,…