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Junior AI Engineer, Agentic Systems
Not specifiedFull-timeAI EngineeringSemiconductorsEnterprise Software
Build and productionize LLM-powered agents and software for an AI infrastructure delivery organization. Develop workflows that improve project visibility, automate repetitive tasks, and support reliable operations.
Responsibilities
- Build and productionize agentic AI solutions, tools, and applications.
- Develop LLM agents, tool-calling workflows, orchestration logic, backend services, APIs, data pipelines, and automation features.
- Translate field, delivery, operations, and product needs into technical solutions and working software.
- Create agents that reason across project data, knowledge bases, operational systems, logs, reports, and delivery workflows.
- Build workflows for risk identification, project-status summaries, task automation, readiness visibility, and handoffs.
- Apply retrieval-augmented generation, context management, agent memory, function calling, evaluations, and guardrails.
- Integrate agents and LLMs with internal systems, data sources, knowledge repositories, reporting tools, and operational workflows.
- Collaborate with engineering, architecture, product, DevOps/SRE, and field teams to deliver reliable, scalable solutions.
- Contribute to engineering standards for code quality, testing, CI/CD, observability, documentation, security, and production support.
Requirements
- Bachelor’s degree in computer science, computer engineering, or a related technical field, or equivalent experience.
- At least 2 years of software development experience building production applications, platforms, automation tools, or AI systems.
- Strong Python and modern backend development skills.
- Hands-on experience with LLMs, agent workflows, tool or function calling, retrieval-augmented generation, and AI application development.
- Experience building REST APIs, data services, workflow automation, and enterprise integrations.
- Experience testing, evaluating, monitoring, and handling failures in software using non-deterministic AI systems.
- Experience with Docker, Kubernetes, CI/CD, Git, observability, and cloud-native development.
- Knowledge of SQL and NoSQL databases, data modeling, querying, indexing, and data integration.
Nice to have
- Experience with agent platforms, copilots, multi-agent systems, evaluation frameworks, or MCP-style integrations.
- Knowledge of context engineering, retrieval quality, agent planning, human-in-the-loop workflows, and AI safety guardrails.
- Experience with AI infrastructure, HPC clusters, GPU systems, networking, Kubernetes, or SLURM.
- Background automating field delivery, operations, or professional services workflows.
- Experience with Linux, networking, security, SRE, or distributed systems.