Applied AI ML - Python & Agentic AI
On-site · Glasgow, Scotland, United Kingdom
Job Summary
Senior Associate, Applied AI/ML Engineer to design, develop, and productionize GenAI and Agentic AI solutions using Java and/or Python, APIs/microservices, and AWS cloud-native platforms. You’ll build LLM/SLM-powered applications (RAG, summarization/extraction pipelines, chat/coplay experiences) and tool-using agents, develop prompt strategies and automated evaluation, and implement robust ML data pipelines with MLOps across the lifecycle (experimentation, versioning, CI/CD, deployment, monitoring). Collaboration with cross-functional stakeholders to define requirements and rollout plans, plus strong emphasis on secure coding, reliability, and observability. Requires hands-on experience delivering production AI/ML systems and familiarity with MCP, embeddings/vector search, tool calling, and modern engineering practices.
Required Qualifications
- Undergrad or Master’s degree (or equivalent practical experience) in Computer Science, Data Science, Machine Learning, or related field
- Hands-on experience building applied AI/ML or GenAI solutions (e.g., RAG, classification, extraction, ranking, summarization, copilots)
- Familiarity with MCP (Model Context Protocol), Agent Skills and architectures that connect models to tools/data through standardized interfaces
- Familiarity with LLM application patterns: embeddings/vector search, prompt orchestration, tool calling/function calling, safety/guardrails, evaluation
- Strong software engineering experience delivering production systems; ability to design maintainable architectures and write clean, testable code
- Proficiency in Java and/or Python and experience building APIs/services and integrating with data sources and downstream systems
- Experience deploying solutions on AWS and cloud-native environments; understanding of security fundamentals and operational excellence
- Experience with modern engineering practices: CI/CD, code reviews, unit testing (e.g., pytest/JUnit), and deployment automation
- Experience with containers and orchestration (e.g., Docker, Kubernetes/EKS) and production monitoring practices
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