Lead Agentic AI Engineer – Enterprise Data Platform (EDP)

About MultiBank Group

Role overview

Agent design and delivery

• Build and deploy single- and multi-agent systems for workflow automation and decision support across business domains.
• Implement tool use, planning, memory, RAG, and multi-agent orchestration.
• Integrate enterprise data, CRM, back-office, payment, support, and marketing systems using APIs and MCP servers.
• Identify use cases and success metrics with business stakeholders.

Reliability, platform, and engineering

• Create evaluation frameworks for task success, accuracy, groundedness, regression, cost, and latency.
• Implement guardrails, human-in-the-loop checkpoints, permission scoping, and fallbacks for sensitive workflows.
• Monitor production agents for quality, drift, hallucinations, failures, and misuse; continuously improve them.
• Build and operate AWS agent and LLM workloads; integrate foundation models with attention to quality, cost, and data residency.
• Collaborate on scalability, identity and access, secrets management, observability, and cost control.
• Implement CI/CD, versioning, automated testing, and release practices; create reusable components, SDKs, and templates.

Data, governance, and leadership

• Work with data engineers on curated enterprise data and governed data or semantic layers.
• Define retrieval and knowledge strategies using vector stores, structured query tools, and guarded text-to-SQL.
• Ensure compliance with AI governance, data classification, privacy, and regulatory requirements; keep sensitive data within approved boundaries.
• Provide auditability, traceability, and explainability; contribute to governance standards and approvals.
• Lead and mentor engineers, run design and code reviews, define roadmaps and architecture, and communicate trade-offs to stakeholders.

Experience and education

• At least 8 years in software, ML, or AI engineering, including at least 2 years building LLM or agentic systems and one production delivery.
• Bachelor’s degree in Computer Science, Engineering, Mathematics, Data Science, or a related field.
• Experience leading or mentoring engineers is preferred; technical lead experience is preferred.
• A master’s degree or relevant AI certifications is a plus.

Technical qualifications

• Production experience building LLM-based agents using tool/function calling, orchestration, RAG, memory, or multi-agent workflows.
• Hands-on experience with agent frameworks and protocols such as LangGraph, LangChain, LlamaIndex, Strands, Claude/OpenAI agent SDKs, and MCP.
• Strong prompt and context engineering, LLM evaluation, and observability experience; understanding of guardrails, prompt-injection risks, and safe tool permissions.
• Strong AWS Bedrock experience, plus Lambda, API Gateway, IAM, Docker, and containerized deployments; ECS/EKS are a plus.
• Expert Python and API/service development with FastAPI or similar; ability to write clean, tested production code.
• Familiarity with open-source ML/NLP libraries and vector databases or search.
• Solid grounding in data pipelines, warehousing, and SQL; Redshift, Snowflake, or Databricks experience is a plus.
• SageMaker or other ML platform services and data-governance tooling experience are advantages.

Domain and leadership qualifications

• Financial services, trading, FX, crypto, or regulated-industry experience is preferred.
• Understanding of AI data privacy, compliance, and risk considerations.
• Ability to deliver independently, lead and mentor engineers, set technical standards, translate business problems into agent designs, and explain AI concepts to non-technical leaders.

Why join

• Build and lead the group’s agentic AI capability and own production delivery across business functions.
• Work with a governed enterprise data foundation and executive sponsorship, shaping AI architecture and governance.
• The employer advertises competitive salary, benefits, professional growth, and a collaborative work environment.