Senior AI-Native Software Developer (Remote)

Cyrantos is partnering with a healthcare and financial intelligence platform to recruit a Senior AI-Native Software Developer. The role involves building production-grade agentic workflow platforms for healthcare data analytics and financial portfolio intelligence, integrating AI agents with business rules, statistical models, APIs, and human approval workflows.

• Strong commercial experience with Python and modern backend development
• Experience building cloud-native systems, preferably on AWS
• Practical experience integrating LLMs and developing agentic or AI-enabled applications
• Knowledge of APIs, event-driven architecture, asynchronous processing, and distributed systems
• Experience with relational databases, data pipelines, and structured data processing
• Strong understanding of software quality, security, testing, and production observability
• Ability to distinguish between tasks suitable for LLMs and those requiring deterministic implementation
• Experience with LangGraph, Strands Agents, Temporal, or AWS Step Functions is a plus
• Hands-on experience with Amazon Bedrock, OpenAI, or Anthropic APIs is beneficial
• Knowledge of RAG, vector databases, embeddings, and reranking is a plus
• Familiarity with ML pipelines (MLflow, SageMaker), compliance, data lineage, model governance, or human-approval workflows is beneficial

Nice to have:

• Exposure to healthcare and financial industries

• Design and implement multi-agent workflows using frameworks such as LangGraph, Strands Agents, or OpenAI Agents SDK
• Integrate agent workflows with AWS Step Functions, EventBridge, SQS, Lambda, and container-based services
• Build secure tools and APIs used by AI agents to access data, calculations, optimization engines, and compliance rules
• Integrate LLMs through Amazon Bedrock, OpenAI, Anthropic, or similar platforms, implementing structured outputs, model routing, fallbacks, prompt versioning, and cost controls
• Develop RAG pipelines using approved internal, regulatory, clinical, or financial knowledge sources
• Connect AI components with deterministic Python services, statistical models, rules engines, and optimization algorithms, implementing human-in-the-loop review, audit trails, observability, and model evaluation
• Contribute to architecture decisions, code reviews, testing, deployment, and engineering standards

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