We are seeking a highly skilled AI Engineer with a Master’s degree in Computer Science, Artificial Intelligence, or a related field to design, develop, and deploy advanced AI/ML systems. This role is centered on building next-generation agentic AI solutions powered by retrieval-augmented generation (RAG), leveraging modern orchestration frameworks such as LangGraph and Model Context Protocol (MCP).
The ideal candidate will have deep expertise in Python-based AI development and hands-on experience designing agent systems capable of reasoning, planning, tool usage, and executing complex multi-step workflows. A strong foundation in end-to-end RAG architectures, including Graph RAG, is required.
Maximise your chances of a successful application to this job by ensuring your CV and skills are a good match.
Primary Skill: Artificial Intelligence/Machine Learni
ng Secondary Skill: Pyth
on Tertiary Skill: Natural Language Processi
n
g Required Qualificatio
nsMaster’s degree in Computer Science, Artificial Intelligence, Machine Learning, or a related fiel
d.Strong proficiency in Python programming, with experience building scalable AI/ML system
s.Hands-on experience with agentic AI frameworks, particularly LangGraph, and emerging standards such as Model Context Protocol (MCP
).Strong experience designing and implementing advanced RAG architectures, including Graph RA
G.Experience with LLM orchestration frameworks such as LangChain, LangGraph, and LlamaInde
x.Proven experience deploying LLM-powered production system
s.Design and implement advanced RAG pipelines using vector databases, embeddings, knowledge graphs, and hybrid retrieval strategie
s.Develop agentic AI systems using LangGraph, enabling dynamic task planning, reasoning, tool orchestration, and multi-agent workflow
s.Integrate Model Context Protocol (MCP) for standardized context sharing, tool interoperability, and scalable agent communicatio
n.Design memory systems and contextual state management for agent continuity and long-running workflow
s.Implement evaluation pipelines, prompt engineering strategies, and guardrails to ensure performance, safety, and reliabilit
y.Apply Model Risk Management (MRM) practices across the AI lifecycle, including model validation, explainability, bias detection, monitoring, and documentatio
n.Strong experience with Python ML/AI frameworks such as PyTorch, TensorFlow, and Scikit-lear
n.Hands-on experience with vector databases (FAISS, Pinecone, Weaviate, Azure AI Search) and semantic retrieval system
s.Deep understanding of agent orchestration patterns, including planning, reflection, tool usage, and multi-agent collaboratio
n.Experience implementing Graph RAG using knowledge graphs and structured data integratio
n.Expertise in memory architectures (short-term, long-term, episodic memory) in agent system
s.Strong understanding of LLMOps/MLOps, including CI/CD, observability, monitoring, and performance optimizatio
n.Working knowledge of Model Risk Management (MRM) frameworks including governance, validation, and lifecycle control
s.Familiarity with AI safety and alignment techniques, including guardrails, human-in-the-loop systems, and bias mitigatio
n.Experience with model evaluation, benchmarking, and explainability tools
.Proficiency with development tools such as GitHub, VS Code, JIRA, and modern engineering workflow
s. xsgimln
Desired Qualificati
onsExperience working in an Agile development methodology; experience with RAG and
LLM
Intake No
tes:Overview of the work being
doneDesign and develop production-grade Python APIs/serv
icesDeploy and operate applications on OpenS
hiftPartner with AI/ML engineers to productionize model capabilities into usable backend serv
icesRemediate vulnerabilities
in:Python libraries/dependen
ciesContainer im
agesOpenShift deployment configurat
ionsPrimarily internal collaboration with cross-functional teams such as AI/ML engineers, UI developers, DevOps, and security/compliance stakehold
ers.Building Python-based microservices/APIs that expose AI/ML model functionality to downstream applicat
ionsDeploying containerized applications to OpenShift and configuring manifests, services, routes, and sec
retsIntegrating backend APIs with Angular-based front-end applicat
ionsPerforming remediation of security findings in Python dependencies and container im
agesAutomating deployment workflows using CI/CD pipelines aligned with OpenShift stand