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

Qualifications, work experience:

• 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.
• 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.

Soft Skills:

• End-to-end ownership mindset
• Strong documentation and communication
• Comfortable with incident response and performance tuning

Location: Bangalore

Mode of work: Work from Office