Senior AI Deployment Engineer — Agentic AI

BMC Software; England; Hybrid; GBP 100,000 - 150,000; Full time

Job summary

BMC Software is seeking a senior engineer to deploy and customize agentic AI in diverse customer environments. You will own features end‑to‑end, integrate with customer systems and cloud platforms, and support pilots across regions with travel as needed.

You will work with enterprise clients, applying Python, API integration, and DevOps practices while shaping pragmatic engineering standards and field learnings for product improvements.

Qualifications

• Typically 8–13 years of relevant experience available.
• Bachelor’s or Master’s in Computer Science, Engineering, or related field.
• Proficiency in Python and software‑engineering fundamentals.
• Familiarity with enterprise systems/API integration and cloud platforms.

Responsibilities

• Deploy, configure, and customize agentic AI in customer environments (on‑prem, hyperscaler, or OpenShift).
• Lead end‑to‑end feature ownership and integrations amid evolving requirements.
• Build integrations with customer systems and cloud platforms (AWS, Azure, GCP).
• Support proofs‑of‑concept and pilots, removing blockers and informing stakeholders.
• Collaborate with customers across regions/time zones and travel as needed.
• Serve as a trusted technical advisor on agentic‑AI adoption with human oversight.

Skills

Python

LLM/RAG/agents/prompt engineering

Communication skills

Travel willingness

Multi-team collaboration

Delivery standards

Rapid prototyping

Ownership

Education

Bachelor’s or Master’s in Computer Science or related field

Tools

Docker

Kubernetes

OpenShift

AWS

Azure

GCP

CI/CD tools

Job description

BMC Software is seeking a senior engineer to deploy and customize agentic AI in diverse customer environments. You will own features end‑to‑end, integrate with customer systems and cloud platforms, and support pilots across regions with travel as needed.

You will work with enterprise clients, applying Python, API integration, and DevOps practices while shaping pragmatic engineering standards and field learnings for product improvements.