AI ML Ops
Summary
Build and deploy production-grade AI services, optimize MLOps pipelines, and engineer prompts for LLM applications using Java, React, and Kubernetes.
AI/ML Ops
Location - Sheffield
Software Engineering Delivery
o Design Build and ship production quality services within our current business controls
o Build and Optimise DecOps pipelines enhancing CICD processing including testing and platformprocess observability
o Design iterate and optimise prompts for a wide range of LLM use cases instruction following structured output generation classification summarisation code generation and reasoning tasks
o Build and manage prompt libraries and versioning strategies treating prompts as firstclass engineering artefacts
o Collaborate with product and domain teams to translate business requirements into precise wellstructured prompt specifications
Agentic AI LLM Integration
- Architect and implement maintain agentic systems autonomous agents multiagent pipelines tool use workflows planning loops and memory augmented reasoning
- Implement LLM observability logging tracing evaluation pipelines and guardrails to maintain quality and reliability in production AI systems
- Advise on responsible AI deployment including output validation human in the loop design and risk mitigation for agentic workflows.
Technical Skills Experience
FullStack Engineering
- Strong proficiency in Java Spring Boot REST APIs eventdriven services security and performance optimisation.
- Solid React development skills and API integration
- Experience with relational and in memory databases PostgreSQL Redis
- Messaging and streaming platforms Kafka or equivalent
Prompt Engineering
- Demonstrable handson experience engineering prompts for production LLM applications not just prototypes
- Familiarity with prompt evaluation frameworks and tooling PromptFlow LangSmith Braintrust or custom eval pipelines
- Understanding of tokenisation context limits temperature tuning and modelspecific behaviours across major LLM providers
- Experience structuring prompts for toolfunction calling structured JSON output and multiturn conversations
DevOps Infrastructure
- Kubernetes deployments services ingress Helm Kustomize HPA and RBAC
- CI/CD pipelines GitHub Actions Git Lab CI Jenkins or equivalent
- Infrastructure as code Terraform
- Cloud platforms AWS GCP or Azure
Skills
Mandatory Skills : MLOPS, Python