Senior Python Engineer (AI)
Velocity Tech
Greater London, UK
13 days ago
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Role details
Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Experience level
Expert
Experience required
8 years minimum
Working hours
Regular working hours
Job source
Tech stack
Application Programming Interfaces (APIs)
Artificial Intelligence
Amazon Web Services
Microsoft Azure
Software as a Service
Cloud Computing
Distributed Systems
Python (Programming Language)
Machine Learning
Enterprise Data Management
Enterprise Software Applications
Large Language Models
+6 more
Multi-Agent Systems
Backend
Build Management
Kubernetes
Data Management
Virtual Agents
Job description
Velocity Tech Partners are hiring a Senior Python Engineer to join a high-performing team building cutting-edge agentic AI platforms for enterprise clients.
This is a senior-level role focused on designing and delivering production-grade AI systems using modern LLM orchestration frameworks - not ML research.
You’ll work across complex client environments, helping organisations deploy intelligent, autonomous systems that drive real operational impact.
What you’ll be doing
- Design and build agentic AI platforms using Python and modern AI frameworks
- Architect LLM orchestration using tools such as LangGraph, CrewAI, AutoGen and similar technologies
- Build and scale multi-agent systems with tool use, memory, planning, and workflow execution
- Design and implement RAG pipelines over enterprise data sources
- Integrate agentic systems into client environments (APIs, data platforms, SaaS tools, enterprise systems)
- Deploy and manage workloads in Kubernetes across cloud, on-prem, and hybrid infrastructures
- Design robust, scalable, and secure production architectures
- Work directly with clients and stakeholders to translate business problems into technical solutions
Requirements
- 8+ years’ experience as a Senior Software Engineer (Python-focused)
- Strong track record building production-grade backend systems
- Proven experience delivering agentic AI / LLM-based systems in production
- Hands-on experience with RAG pipelines and enterprise data integration
- Strong distributed systems and architecture skills
- Experience deploying workloads in Kubernetes
- Cloud experience (AWS / Azure / GCP)
- Comfortable working in fast-paced, ambiguous, client-facing environments
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