AI Engineer – AI Agents & Infrastructure

What You’ll Do

• Build and enhance AI agents and agentic workflows using Python, Go language, LLMs, and modern AI frameworks.
• Develop backend services, APIs, and integrations that support production AI applications.
• Work with Agent SDKs and Model Context Protocol (MCP) to connect agents with tools, data, and services.
• Help build AI guardrails, controls, monitoring, tracing, and observability to make AI applications safer and more reliable.
• Partner with senior AI and software engineers to turn prototypes into scalable, production-ready applications.
• Build and test integrations with GCP/AWS, APIs, databases, and containerized services.
• Experiment with new LLMs, agent frameworks, AI developer tools, and automation technologies.
• Participate in code reviews, testing, debugging, deployment, and technical design discussions.
• Learn how production AI systems are secured, monitored, evaluated, and governed.
• Build, deploy, and operate containerized AI services and agent workloads on Kubernetes in cloud environments such as GCP/AWS.

What We’re Looking For

• Recent graduate or 0–2 years of software engineering experience.
• Bachelor's or Master's degree in Computer Science, Software Engineering, AI/ML, Data Science, Computer Engineering, or similar technical discipline.
• Strong programming fundamentals with Python.
• Understanding of APIs, backend development, databases, and basic software architecture.
• Hands-on exposure to LLMs, generative AI, AI agents, or agentic workflows through internships, university projects, research, hackathons, or personal projects.
• Familiarity with at least one AI/LLM framework or SDK such as OpenAI SDK, Anthropic, LangChain, LangGraph, CrewAI, AutoGen, or similar.
• Basic understanding of cloud technologies; GCP/AWS experience is a plus.
• Interest in learning MCP, AI security, guardrails, observability, and agent control systems.
• Exposure to Kubernetes, Docker, and cloud-native application deployment
• Strong problem-solving skills and curiosity about how AI systems work under the hood.
• Comfortable learning quickly and working closely with experienced engineers in a fast-moving environment.

Preferred Qualifications

• Internship, research, or project experience building an AI agent, RAG application, chatbot, copilot, or AI automation.
• Experience with Git, Docker, Linux, REST APIs, SQL/NoSQL, or AWS.
• Exposure to MCP or Agent SDKs.
• Familiarity with prompt engineering, embeddings, vector databases, RAG, or tool/function calling.
• Exposure to AI evaluation, hallucination reduction, security guardrails, or LLM observability.
• Coursework or projects involving machine learning, distributed systems, cloud computing, cybersecurity, or backend engineering.