Cognizant
AI Systems Engineer
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Senior AI Systems Engineer
The Senior AI Systems Engineer builds and operates the intelligence layer of client AI products. The role is software-engineering led, not prompt-development led: it makes model, retrieval, tool and orchestration behavior testable, evidence-grounded, observable and reliable in production.
Primary accountability: Own the implementation of the product’s AI systems – model integration, retrieval and context assembly, multi-step workflows, evaluation instrumentation, provenance mechanics and failure handling.
Success looks like:
- AI outputs are grounded in data, and their supporting sources can be inspected wherever the risk tier requires it.
- Task, retrieval, citation and workflow quality are measured through repeatable evaluation and regression suites, with hooks exposed for independent evaluation.
- Prompts, models, orchestration logic, datasets, configurations and evaluation assets are versioned production artefacts.
- Known failure modes have explicit controls, abstention or escalation behaviour, and observable operational signals.
Key responsibilities:
- Design, build and operate foundation-model, retrieval, and workflow services that solve the product’s operational problem.
- Build context and retrieval pipelines, structured outputs, evidence references and citation verification appropriate to the use case.
- Implement multi-step workflows with explicit human review, approval, escalation and safe-failure (abstention) paths.
- Work with stakeholders to create evaluation datasets and automated tests for task success, retrieval quality, source support, robustness, latency, cost and regression.
- Ensure and uphold security conscious coding standards building robustness against prompt injection, unsafe tool use, access-control failure, data leakage, unsupported claims, retrieval failure and model or configuration degradation using appropriate 3rd party tooling.
- Work with Platform Engineering on CI/CD, release, runtime, observability and support, and with the Technical Lead on architecture and engineering quality.
- Review supplier code and artefacts, maintain documentation and ensure knowledge transfer into permanent client ownership.
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What you will get from the role
Specific insights or unique benefits the role offers:
- The opportunity to build production-grade AI systems that solve real regulatory problems, working on products that are embedded into client operational workflows rather than standalone proofs of concept or demonstrations.
- Hands-on experience at the forefront of enterprise AI engineering, including retrieval-augmented generation (RAG), agentic workflows, orchestration, evaluation frameworks, provenance, observability and secure AI system design.
- The chance to help shape how a major financial regulator applies AI safely and responsibly, building systems that are evidence-grounded, measurable, auditable and designed to support meaningful human judgement.
- Exposure to complex engineering challenges that extend well beyond model integration, including retrieval quality, failure handling, evaluation, governance, security, operational resilience and long-term maintainability.
- The opportunity to work alongside experienced engineers, architects, product leads and domain specialists while contributing to a growing AI capability that is intended to have impact across Authorisations, Supervision, Enforcement and AML activities.
Minimum criteria
Required proven experience in a specific technical area or skill:
- Strong Python and production software engineering, including APIs, automated testing and cloud-native services.
- Practical experience building and operating production applications using foundation models and LLM APIs. Strong understanding of prompt engineering techniques and best practices.
- Experience with retrieval and embedding pipelines, semantic search or equivalent AI data pipelines.
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Essential criteria
- Evaluation and monitoring of AI systems as a practice – golden datasets, regression suites, retrieval and citation quality measures, human and automated evaluation techniques – rather than ad hoc demonstration.
- Designing evidence-grounded, human-authorised workflows with explicit failure, abstention and escalation behaviour.
- Experience with data modelling, schema definition, and validation frameworks.
- Working knowledge of AI failure modes – hallucination, retrieval drift, context failure, injection, degradation – and their controls.
- CI/CD, version control and operational telemetry, with prompts, configurations, datasets and evaluation assets treated as versioned production artefacts.
- Secure engineering: access control, data-leakage prevention and safe tool use.
- Communicating technical trade-offs within a multidisciplinary pod, reviewing supplier work and transferring knowledge.
- Translating non-technical business requirements from stakeholders into scalable, maintainable, robust solutions.
Particularly valuable:
- AWS and Bedrock, or comparable cloud and foundation-model platforms.
- Extracting and structuring information from unstructured documents using OCR or multimodal foundation models
- Agent orchestration, DAG orchestration, event-driven systems, graph-enhanced retrieval, or temporal and provenance models – including S3 metadata and annotation approaches.
- Regulatory technology, financial services or other controlled operational environments.
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