OIP Insurtech

🚀 Why join Bound AI (OIP Insurtech)?

We build real-world AI workflows that transform insurance operations—from underwriting to policy issuance. You’ll join a fast-growing, global team of engineers and innovators tackling the toughest problems in document intelligence and agent orchestration. We move fast, ship impact, and value autonomy over bureaucracy.

🧭 What You'll Be Doing

• Design and deliver end‑to‑end AI solutions: from 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

• 6+ years of experience delivering technical solutions in machine learning, AI engineering or solution architecture.
• Proven track record leading design, deployment, and integration of GenAI-based systems (LLM tuning, RAG, multi-agent orchestration).
• Fluency with Python production code, cloud platforms (AWS, GCP, Azure), and container orchestration tools.
• Excellent communication skills—able to bridge technical strategy and business outcomes with clarity.
• Startup mindset—resourceful, proactive, and hands‑on when needed.
• Bonus: experience with insurance-specific workflows or document intelligence domains (SOVs, loss runs, ACORD forms).

🛠️ Core Skills & Tools

• 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, and retraining pipelines.
• Data engineering: preprocessing, structured/unstructured data integration, pipelines.
• Infrastructure: Kubernetes, Docker, cloud deployment, serverless components.

📈 Why This Role Matters

As an AI Solution Architect, you’ll shape the blueprint for how AI transforms insurance workflows—aligning product strategy, operational impact, and technical scalability. You're not just writing code; you’re orchestrating systems that make labor-intensive processes smarter, faster, and more transparent.

We’re looking for a skilled Senior Machine Learning Engineer to help us transform the Insurtech space. You’ll build intelligent agents and models that read, reason, and act.

Insurance ops are broken. Underwriters drown in PDFs. Risk clearance is chaos. Emails go in circles. We’ve lived it – and we’re fixing it. Bound AI is building agentic AI workflows that go beyond chat. We orchestrate intelligent agents to handle policy operations end-to-end:

• Risk clearance.

• SOV ingestion.

• Loss run summarization.

• Policy issuance.

• Risk triage.

No hallucinations. No handwaving. Just real-world AI that executes – in production, at scale.

Join us to help shape the future of insurance through advanced technology!

We’re Looking For:

• Deep experience in GenAI, LLM fine-tuning, and multi-agent orchestration (LangChain, DSPy, or similar).
• 5+ years of proven experience in the field
• Strong ML/AI engineering background in both foundational modeling (NLP, transformers, RAG) and traditional ML.
• Solid Python engineering chops – you write production-ready code, not just notebooks.
• A startup mindset – curiosity, speed, and obsession with shipping things that matter.
• Bonus – Experience with insurance or document intelligence (SOVs, Loss Runs, ACORDs).

What You’ll Be Doing:

• Develop foundation-model-based pipelines to read and understand insurance documents.
• Develop GenAI agents that handle real-time decision-making and workflow orchestration, and modular, composable agent architectures that interact with humans, APIs, and other agents.
• Work on auto-adaptive workflows that optimize around data quality, context, and risk signals.