Senior Python Engineer (AI)

Be a key player at the forefront of the digital assets movement, propelling your career to new heights! Join a dynamic and rapidly expanding company that values and rewards talent, initiative, and creativity. Work alongside one of the most brilliant teams in the industry.

• 3–5+ years of professional experience as a Python Developer, AI Engineer
• Strong proficiency in Python 3.x, including asynchronous programming, REST API development
• Experience with frameworks such as FastAPI, FastMCP, Flask, or Django
• Practical experience building or integrating AI-powered solutions in production or near-production environments
• Experience working with LLM APIs and AI platforms, such as OpenAI, Anthropic, AWS Bedrock, Google Vertex AI, or similar
• Experience designing AI agent workflows, tool-calling flows, prompt-driven automation, or multi-step AI orchestration
• Understanding of AI engineering concepts such as prompt design, retrieval-augmented generation, embeddings, vector databases, structured outputs, evaluation, and guardrails
• Ability to integrate AI capabilities into existing services, internal tools, and automation workflows
• Strong experience with API integrations, including authentication, OAuth2, webhooks, error handling, retries, rate limits, and secure data exchange
• Solid understanding of SQL and relational databases such as PostgreSQL or MySQL
• Experience troubleshooting API integrations, data synchronization issues, automation failures, and production defects
• Basic proficiency in HTML, CSS, and JavaScript to support lightweight frontend interfaces or internal admin dashboards
• Experience with Docker and Kubernetes
• Familiarity with GitLab CI/CD or similar CI/CD pipelines
• Understanding of secure coding practices, secrets management, access control, and secure data handling
• Strong analytical, debugging, and problem-solving skills
• Excellent communication skills and ability to work with both technical and non-technical stakeholders

Preferred:

• Experience working with AWS services
• Experience with Kubernetes deployments and management
• Experience integrating with enterprise tools (Jira API, Okta API, Slack API)
• Familiarity with infrastructure as code and DevOps practices
• Exposure to logging and monitoring stacks (ELK, Prometheus, Grafana)
• Understanding of secure coding practices and compliance requirements in fintech or regulated environments
• General understanding of the math and algorithms behind AI and related technologies

Nice to Have:

• Experience designing human-in-the-loop AI workflows with approvals, escalation paths, and audit trails
• Experience building chat-based internal assistants or AI copilots connected to company data and systems
• Experience with financial data processing, reconciliation, reporting automation, or transaction monitoring
• Understanding of risk, compliance, and operational controls in financial environments

• Develop and maintain internal services, tools, APIs, automation scripts, and AI-enabled workflows.
• Design and implement AI agents, multi-step agent flows, and automation pipelines to support internal business processes.
• Build integrations with internal and third-party systems, including financial platforms, payment providers, reporting tools, Jira, Confluence, Okta, Slack, AWS, and other enterprise systems.
• Develop AI-powered features such as intelligent data processing, document analysis, classification, anomaly detection, workflow automation, and decision-support tools.
• Integrate LLMs and AI services into internal applications using APIs, orchestration frameworks, vector databases, and retrieval-augmented generation where appropriate.
• Create and maintain automation flows connecting AI agents, APIs, databases, notifications, and approval workflows, local and remote MCP servers.
• Investigate, troubleshoot, and resolve issues related to financial integrations, API failures, data inconsistencies, automation errors.
• Write clean, efficient, secure, and well-documented code following software engineering best practices.
• Implement CI/CD pipelines and automated testing frameworks.
• Optimize system performance and ensure reliability and security.
• Contribute to architectural decisions and participate in code reviews.
• Optimize performance, reliability, observability, and security of internal tools and integrations.
• Support architectural decisions, participate in code reviews, and contribute to engineering standards.
• Continuously identify opportunities to improve manual processes through automation, AI, and better system integrations.