As an AI Engineer , you will be at the centre of our AI initiatives building and scaling the intelligent systems that make our operations smarter. You will own the MCP layer that connects AI models to our data infrastructure, design and tune AI agent workflows for efficient tool calling, and build production-grade agents that serve teams across the organisation.
This role sits at the intersection of applied AI, data engineering, and platform engineering. You’ll work closely with data engineers, backend engineers, product managers, and operations teams to translate real business problems into AI-powered solutions.
Key Responsibilities :
– AI Agent Development : Design, build, and deploy AI agents that can autonomously query databases, analyse logistics data, perform root cause analysis, and answer business questions for operations, product, and leadership teams.
– MCP Infrastructure : Own and evolve the LoadShare MCP connecting LLMs (Claude, Gemini, etc.) to Redshift, MySQL read replicas, and internal APIs with proper access controls, read-only security patterns, and schema context.
– Agent Architecture & Evaluation : Evaluate multiple agent architectures on output accuracy, latency, and token cost. Drive decisions on the right patterns for different use cases.
– Workflow Tuning : Optimise agent tool-calling workflows for efficiency reducing unnecessary LLM calls, improving context retrieval, and ensuring agents produce reliable, validated outputs.
– Knowledge Layer : Build and maintain the shared context layer (table schemas, business rules, metric definitions, join patterns) that powers all AI tools across the org, creating a single source of truth.
– Voice & Conversational AI : Contribute to AI-powered voice agents for grievance classification, rider support, and customer interaction using models like Gemini Live and Claude.
– Internal Tooling & Enablement : Support the broader engineering team in adopting AI tools building skills, MCP connectors, and reusable skills that make everyone more productive.
– Security & Access Control : Implement robust security patterns for AI-to-database access, including read-only user provisioning, IAM role mapping, DDL restrictions, and query guardrails.
Required Experience & Qualifications :
– 0 to 3 years of experience in software engineering with a strong focus on AI/ML applications or data engineering.
– Hands-on experience building AI agents or LLM-powered applications in production (not just prototypes).
– Proficiency in Python; working knowledge of JavaScript/Node.js is a plus.
– Experience with LLM APIs and frameworks OpenAI, Anthropic (Claude), Google (Gemini), or similar.
– Strong SQL skills and experience working with data warehouses (Redshift, BigQuery, or Snowflake).
– Familiarity with agent frameworks such as LangChain, LlamaIndex, Agno, CrewAI, or Claude Agent SDK.
– Understanding of RAG architectures, vector databases, and context management strategies.
– Experience with AWS services (IAM, Redshift, EC2, SSM) and database access control patterns.
– Solid grasp of API design, security best practices, and production deployment workflows.
Good to Have :
– Experience with MCP (Model Context Protocol) or similar tool-use patterns for LLMs.
– Familiarity with n8n, Metabase, or similar workflow/analytics platforms.
– Experience building voice AI or conversational AI systems.
– Background in logistics, supply chain, or operations technology.
– Contributions to open-source AI projects or published work in applied AI.
– Experience with agent evaluation frameworks and benchmarking methodologies.
– Familiarity with CI/CD pipelines (Jenkins, GitHub Actions) and infrastructure-as-code
Are you interested in this position?
Apply by clicking on the “Apply Now” button below!
#AlbionarcJobs#FintechJobs
#AsiaJobs#MiddleEastCareers
#TechTalent#FintechRecruitment
#FinanceOpportunities#
