AI Solutions Architect

AI Solutions Architect

Serbia/North Macedonia/India (Hybrid/Remote)

We are looking for an AI Solutions Architect to design, deliver, and lead the deployment of cutting-edge GenAI systems in the insurance domain.

What You’ll Be Doing

  •  Design and deliver end-to-end AI solutions, from the intake of SOVs, loss runs, and documents to deployed intelligent agent workflows

  • Collaborate closely with stakeholders (product, operations, engineering) to architect scalable ML & GenAI systems that solve real insurance challenges

  • Translate business needs into architecture diagrams, data flows, and system integrations

  • Choose and integrate components such as RAG pipelines, LLM orchestration (LangChain, DSPy), vector databases, and MLOps tooling

  • Oversee technical proof-of-concepts, pilot projects, and production rollout strategies

  • Establish governance and best practices for model lifecycle, monitoring, error handling, and versioning

  • Act as a trusted advisor and technical leader—mentor engineers and evangelize design principles across teams

What We’re Looking For

  •  5+ years of experience delivering technical solutions in machine learning, AI engineering, or solution architecture

  • Proven track record in leading design, deployment, and integration of GenAI-based systems (LLM tuning, RAG, multi-agent orchestration)

  • Strong programming background with Python and experience in production code
    Hands-on experience with cloud platforms (AWS, GCP, Azure) and container orchestration tools (Docker, Kubernetes)

  • Excellent communication skills with the ability to connect technical strategy to business outcomes

  • Startup mindset—resourceful, proactive, and hands-on when needed
    Experience with insurance-specific workflows or document intelligence (SOVs, loss runs, ACORD forms) is a plus

  • Foundation models, LLM pipelines, and vector-based retrieval (embedding search, RAG)

  • Architecture modeling and integration: APIs, microservices, orchestration frameworks (LangChain, Haystack, DSPy)

  • MLOps: CI/CD for models, monitoring, feedback loops, retraining pipelines

  • Data engineering: preprocessing, structured/unstructured data integration, pipelines

  • Infrastructure: Kubernetes, Docker, cloud deployment, serverless components














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