Senior Software Engineer/AI Engineer

Senior Software Engineer/AI Engineer: Build production AI systems that solve real insurance and enterprise problems. Experience: 5–7 years. Role orientation: Senior hands-on individual contributor. Primary focus: End-to-end delivery of production AI solutions.

Responsibilities

End-to-end delivery of production AI solutions; Solution design: Work with product and business teams to convert a business workflow, decision or pain point into a practical AI solution and measurable acceptance criteria; End-to-end engineering: Build complete small solutions, including the AI layer, backend services, data stores, APIs and a functional web interface where required; AI workflow orchestration: Design reliable multi-step workflows involving foundation models, retrieval, machine-learning models, rules, external tools and human approvals; Integration: Connect AI services with customer applications and enterprise systems using secure APIs, events or queues; Production readiness: Implement testing, deployment, monitoring, fallback behaviour, audit logging and operational support for live customer use; Performance and economics: Monitor latency, throughput, model usage and infrastructure cost; improve model selection, caching, batching and execution patterns; Trust and control: Build explainability, confidence handling, human-in-the-loop review and traceable decision evidence into the workflow; Technical leadership: Review the work of junior engineers, establish sound engineering patterns and make day-to-day technical decisions within the workstream; Customer demonstrations: Demonstrate working solutions and answer technical questions clearly for customer stakeholders

Requirements

5–7 years of software engineering, data/ML engineering or applied AI experience, including recent hands-on AI delivery; Evidence that you personally built and deployed a live, multi-step AI workflow in a regulated or control-sensitive environment, integrated with business systems and supported by human review and an audit trail; Strong Python engineering skills and experience building production backend services and REST APIs using frameworks such as FastAPI, Flask or an equivalent; Hands-on experience using commercial foundation-model APIs, structured outputs, tool/function calling, prompt design and systematic evaluation; Practical experience with retrieval-augmented generation: document ingestion, chunking, embeddings, search/reranking, vector stores, citations and response-quality evaluation; Working knowledge of conventional machine-learning development, including data preparation, feature engineering, model evaluation and safe handling of uncertain predictions; Experience with SQL plus a non-relational or vector data store, with sound storage-design judgement; Ability to build a basic web interface using React, Streamlit or a comparable framework; Experience deploying containerised services on a major cloud platform using CI/CD, logging and monitoring; A production mindset covering security, failure handling, retries, observability, versioning and maintainability; Ability to work with limited or poorly labelled data and define realistic evaluation methods; Clear written and verbal communication, including the ability to explain design choices, trade-offs and limitations during customer demonstrations; End-to-end ownership mindset; Strong documentation and communication; Comfortable with incident response and performance tuning