Agent Application Engineer (Backend / Agentic AI) — Level 1
About Sentiaflow
Sentiaflow is an AI engineering and IT services company that designs and delivers production-grade agentic AI systems. We work at the point where large language models meet real business operations: data, APIs, permissions, workflow state, human decisions, security, evaluation, and measurable outcomes.
Our initial domain focus is healthcare, particularly clinical trials and related operational workflows. These are environments where an impressive demo is not enough. A system must be dependable, traceable, appropriately controlled, and clear about when a human must make the decision.
We are building a specialised engineering organisation across three disciplines:
- Agent Application Engineering
- Agent Platform & Evaluation Engineering
- Applied AI & Model Engineering
The Role
We are hiring strong backend or application engineers who want to specialise in building agentic AI applications. You do not need to arrive with several years of "agent engineer" experience. We are more interested in whether you have built real software, understand how production systems fail, and can learn to use models inside controlled business workflows.
As a Level 1 Agent Application Engineer, you will implement bounded parts of a production workflow under the guidance of an Agent Captain or senior engineer. Your work will connect models to application services, tools, data sources, and human approval points. You will be expected to make every important step observable, testable, and recoverable.
This is not a prompt-writing position, and it is not a generic chatbot role. It is an application engineering role for systems in which some decisions are probabilistic, while the surrounding controls must remain deterministic.
What You Will Work On
The specific client problem will vary, but typical work may include:
- Converting a clinical-operations signal into a bounded workflow that gathers evidence, analyses likely causes, presents options, and routes a recommendation to an authorised person.
- Building services that extract or structure information from trial documents and pass the result through validation and human review.
- Integrating agent workflows with internal APIs, databases, document systems, notification services, and client platforms.
- Implementing workflow state, recovery behaviour, approval gates, permissions, traceability, and scenario-based evaluations.
The work concerns operational decision support and workflow execution. It does not delegate clinical judgment or patient-care decisions to an autonomous model.
What You Will Be Responsible For
- Translate a clearly scoped business workflow into typed inputs, outputs, states, actions, and escalation paths.
- Build reliable application services and tool integrations using Node.js/TypeScript or Python.
- Use an LLM only where model judgment adds value; implement rules, validation, authorisation, and workflow control in deterministic code.
- Design structured model outputs and validate them before they can affect downstream systems.
- Handle partial data, unavailable tools, duplicate events, retries, time-outs, rate limits, and other normal production failure modes.
- Add logging, traces, metrics, and decision records that make the workflow diagnosable.
- Create tests and evaluation cases that measure whether the complete workflow behaves correctly—not merely whether an answer sounds fluent.
- Protect sensitive information and participate in code, design, failure-analysis, and release-readiness reviews.
- Explain your implementation and its trade-offs clearly to engineers, delivery leads, and client stakeholders.
At Level 1, you will not be expected to define the entire client architecture alone. You will be expected to own your assigned module, ask precise questions, surface risks early, and bring it to a production-ready standard with senior review.
What We Are Looking For
Essential Experience
- Approximately 3–6 years of hands-on backend or application engineering experience.
- Evidence that you have built or materially owned production APIs, services, data flows, integrations, or workflow-heavy applications.
- Strong programming ability in JavaScript/TypeScript, Python, Java, C#, Go, or a comparable backend language. Our preference is Node.js/TypeScript, but engineering depth matters more than language loyalty.
- Practical understanding of API design, databases, asynchronous processing, authentication and authorisation, testing, and deployment.
- Ability to reason clearly about state, retries, idempotency, concurrency, permissions, audit trails, and failure recovery.
- Experience debugging real production behaviour rather than only building greenfield demonstrations.
- Clear written and verbal communication, including the ability to explain technical trade-offs without hiding behind framework terminology.
Useful, but Not Mandatory
- Experience integrating LLM or machine-learning capabilities into an application.
- Experience with event-driven systems, state machines, workflow engines, observability, evaluation harnesses, or production incident analysis.
- Experience in healthcare, life sciences, clinical trials, or another regulated or audit-sensitive environment.
Healthcare or clinical-trials experience is preferred, not required at Level 1. We would rather hire a strong production engineer who can learn the domain than a candidate who knows the vocabulary but cannot build dependable systems.
What Does Not Qualify by Itself
Any of the following may be useful experience, but none is sufficient on its own:
- A chatbot or "chat with your documents" application.
- A RAG demonstration built primarily by connecting frameworks.
- A list of AI tools, model names, courses, certificates, or prompt-engineering techniques.
- An application that works in a demo but has no clear handling of permissions, failures, evaluation, or production operations.
How We Will Assess Fit
Our evaluation is designed to identify engineering judgment without asking you to build unpaid project work. We will focus on:
- A structured discussion of a production system you have worked on.
- Questions grounded in your own CV and claimed experience.
- A realistic workflow scenario covering system boundaries, evidence, controls, failure modes, and testing.
- Backend fundamentals and how you would apply them to an AI-enabled workflow.
You are NOT expected to have built a clinical-trials agent before applying.
What Success Looks Like
Within your first six months, you should be able to:
- Implement a bounded agent-workflow module from an agreed design and take responsibility for its quality.
- Integrate models and tools without allowing probabilistic output to bypass deterministic controls.
- Produce traces, tests, and evaluation evidence that support a release decision.
- Diagnose failures across application, tool, data, and model boundaries with progressively less supervision.
- Explain the workflow to a client or domain stakeholder in clear operational language.
Consistent performance at this level creates a path towards Level 2 — Independent Delivery Engineer, where you own a production workflow end to end, including discovery, architecture, integrations, evaluation, failure handling, and release readiness.
Why You Should Join Us
- Build the real systems behind agentic AI. You will work beyond demos and wrappers on the engineering problems that determine whether an AI workflow can operate in production.
- Develop a scarce, durable specialisation. Sentiaflow offers a defined progression across application engineering, platform and evaluation, and applied AI—not a vague instruction to "learn AI."
- Work on consequential operational problems. Clinical-trials workflows demand evidence, traceability, human accountability, and reliable execution.
- Learn through delivery with experienced review. You will own meaningful engineering work while receiving architectural and domain guidance appropriate to your level.
- Influence how the discipline is built. We are at an early stage, so strong engineers will help shape our methods, reusable assets, quality standards, and engineering culture.
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