Senior GenAI Engineer – AI, Analytics & Automation Environments Strategy

• Build GenAI platform components: Develop production-grade services and integrations across LLM gateways model access patterns retrieval and RAG MCP and tool integration agent-style workflows evaluation observability and guardrails.
• Translate use cases into architecture: Work with engineering product data security and business stakeholders to turn practical needs into clear technical designs and implementation plans.
• Evaluate technical trade-offs: Compare approaches across GenAI architectures including hosted versus self-hosted models gateway technologies RAG patterns agent and tool integration evaluation strategies and framework versus custom implementation choices.
• Integrate internal systems data and tools: Help connect approved data sources platforms APIs models and enterprise tools into AI-enabled workflows in a secure and maintainable way.
• Operationalize what you build: Design for authentication authorization rate limiting cost tracking monitoring fallback reliability quality evaluation and supportability from the start.
• Contribute to team direction: Bring technical depth challenge assumptions document decisions and help shape the platform roadmap through hands-on learning.

• Typically 5-10 years of experience in software engineering ML engineering platform engineering or a similar technical role.
• Hands-on experience building deploying or operating production GenAI or AI-enabled systems beyond prototypes and notebooks.
• Strong hands-on experience building with LLMs or GenAI systems including model access patterns prompt workflows application integration and production operational considerations.
• Experience with LLM gateways API and service layers or model routing patterns.
• Practical knowledge of RAG embeddings chunking retrieval quality grounding and evaluation.
• Experience integrating AI capabilities with tools APIs enterprise systems or agent-style workflows; MCP experience is a strong plus.
• Familiarity with hosted model APIs self-hosted models open-weight models or traditional ML model deployment patterns.
• Strong Python API design CI/CD cloud infrastructure and production engineering fundamentals.
• Ability to explain technical trade-offs clearly to both engineers and non-technical stakeholders.
• Awareness of security governance monitoring cost control and quality guardrails needed for enterprise AI systems.

• Experience in regulated or highly governed environments.
• Knowledge of security risk and data governance practices.
• Experience with MLOps platforms feature stores model registries or experiment tracking tools.
• Experience operating open-weight models such as vLLM TGI or Ollama.
• Experience with LLM observability platforms ML monitoring and evaluation pipelines.
• Analytics data engineering or applied data science experience.
• Multi-agent or advanced orchestration experience.

What we offer

We give you the freedom to be yourself. We are creating an environment of unique individuals like you with different perspectives on the financial industry and the world. A diverse and inclusive environment in which everyones voice counts and where you can reach your full potential.

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All requests are confidential and will not affect your candidacy.

Dont meet every single requirement At Swift we are dedicated to building a workplace where people can bring their full selves and ideas to the team so if you are excited about this role we encourage you to apply even if you do not meet every single qualification.