Senior AI Agent Engineer

About the role

We are looking for a Senior AI Agent Engineer to design, build, and scale production‑grade AI agents that automate real business operations. You will own the full lifecycle of autonomous systems, from architecture through deployment, monitoring, and continuous improvement, with a focus on reliability, security, and measurable business impact.

Key responsibilities

• Architect and implement multi‑agent systems that decompose goals into actionable sequences with bounded autonomy.
• Develop Retrieval‑Augmented Generation (RAG) pipelines, tool‑calling mechanisms, memory structures, and orchestration layers that keep reasoning, retrieval, execution, and presentation separate.
• Integrate AI agents with enterprise platforms such as ERP, CRM, HRMS, SCM, ITSM, document systems, databases, and third‑party APIs.
• Design robust failure‑handling strategies for hallucinations, infinite loops, tool failures, degraded retrieval, cost overruns, and rate‑limit issues.
• Build observability, monitoring, and cost/action ceilings to ensure agents operate safely and failures are diagnosable.
• Create internal AI operations tooling for orchestration, scheduling, governance, lifecycle management, and analytics.
• Develop dashboards linking AI performance to operational efficiency, productivity, and ROI.
• Mentor engineers, conduct code reviews, and ensure production‑ready delivery of AI solutions.

Required profile

• 3+ years of professional software engineering experience building enterprise AI agents or intelligent automation platforms.
• Proven track record shipping AI solutions with measurable business impact in production.
• Hands‑on experience in at least two domains such as customer support, sales, HR, finance, procurement, supply chain, or IT service desk.
• Strong understanding of distributed systems, cloud‑native architecture, APIs, databases, and DevOps practices.
• Bachelor’s or master’s degree in Computer Science, AI, Software Engineering, or a related field.

Required skills

• Distributed systems
• Cloud‑native architecture
• APIs
• Databases
• DevOps
• RAG pipelines
• Tool‑calling mechanisms
• Memory architectures
• Orchestration layers
• Monitoring and observability
• Machine learning model training and fine‑tuning
• Multi‑tenant platform design