Senior AI Engineer

Worldwide | Sept. 1, 2026

Report as Closed

Company: Capital Express

Country: Worldwide

Salary: $18,000 - $24,000

Type: Remote

Employment: Full-time

Description: Financial Services Company with more than 15 years of experience in the factoring industry. None

• Agents: agent architectures (single and multi-agent), tool usage/function calling, structured outputs; harness design—agent loop, context and memory management, sandboxing, error recovery; experience with commercial agent SDKs and frameworks.
• Context engineering: systematic prompt engineering, RAG, repository context for code-based agents (instructions, skills, specs).
• MCP and integrations: building MCP servers; integrating agents with internal and third-party APIs.
• Platforms: generative AI cloud services (e.g., AWS Bedrock), model provider APIs, workflow orchestrators; model selection based on cost, latency, and quality.
• Engineering: Python or TypeScript/Node.js for integrations; REST APIs; knowledge of the internal tech stack to integrate AI into services.
• Evaluation and observability: designing evaluations (including LLM-as-judge), regression suites for agents, traces and quality metrics, guardrails.
• Security: Responsible handling of sensitive data (financial industry) in AI workflows; control of agent actions (permissions, human-in-the-loop where appropriate, defense against prompt injection).

Lead AI implementation and strategy at Capital Express: build agents with business impact, the platform that supports the team’s AI-first development, and guidelines for responsible use. Manage AI infrastructure: model platforms, vendor APIs, automation tools, costs, and quotas.

• Design and build agents based on LLMs: workflow automations and multi-step agents with tool usage, including the harness that executes them (agent loop, context and memory management, error handling, action limits).
• Build and operate MCP servers that expose internal services and data to agents and development tools, with permissions and auditing.
• Design the AI architecture for the development cycle: repository context for code agents, specs as executable contracts, agents in CI/CD, and development flow evaluations.
• Manage the AI infrastructure: model platforms, vendor APIs, automation tools, costs, and quotas.
• Define AI usage guidelines for the team and the company (data security, permitted use cases, quality).
• Evaluate and measure the quality of AI solutions: automated evaluations, traces and monitoring of agents in production, guardrails.
• Train the team on AI tools and code assistants.
• Stay up to date with the ecosystem and propose business use cases (e.g., risk analysis, document processing, customer service).

• Afternoon off on your birthday
• Supplemental insurance
• 3 additional vacation days
• Seasonal fruit

Among other benefits...

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