Why this grade This listing scored 56/100, which is a C. It lost the most ground on pay transparency. See the breakdown
- Description depth 20 / 20 How much the posting actually says about the work, measured in characters of real text.
- Freshness 12 / 15 How recently it was posted. Older postings are likelier to be filled or abandoned.
- Corroboration 10 / 10 Whether more than one source carries this listing.
- Remote clarity 8 / 15 Whether "remote" means anywhere, or is quietly restricted to one country.
- Role specificity 6 / 10 Whether the listing is tagged well enough to tell what the role actually is.
- Pay transparency 0 / 25 A published salary range, worth more than any other single factor because it is what a candidate cannot find out without applying.
Every figure above is arithmetic over the posting itself — its salary field, its text, its age, its tags and how many sources carry it. How the grades work →
This listing does not state a salary
$163k – $276k
That is the middle half of what comparable roles paid on this board over the last 90 days — 31 listings that did publish a figure, median $179k. It is not this employer's offer, and we have no idea what they pay. It is only what the rest of the market advertised.
Data Science Senior Full Time
We are looking for an experienced AI Application Engineer to design and build intelligent applications powered by LLMs, LangGraph, and modern agentic AI technologies.
You will focus on transforming business requirements into AI applications capable of reasoning through tasks, retrieving information, interacting with tools and enterprise systems, requesting human approval when required, and completing business processes.
This role sits at the intersection of AI engineering, software development, workflow automation, and business process transformation.
Requirements
Agentic AI Application Development
- Design and develop AI applications using LangGraph and LLM technologies.
- Build agents capable of executing complex, multi-step business processes.
- Design stateful workflows incorporating reasoning, tool usage, validation, approvals, and exception handling.
- Develop single-agent and multi-agent solutions where appropriate.
- Translate business requirements into practical agentic AI architectures.
LLM Application Engineering
- Integrate LLMs into production applications.
- Develop prompt strategies, structured outputs, tool calling, and context-management approaches.
- Select appropriate models based on accuracy, capability, latency, security, and cost.
- Develop mechanisms to improve reliability and reduce hallucinations.
- Implement appropriate guardrails around AI-generated decisions and actions.
RAG and Enterprise Knowledge
- Design and implement Retrieval-Augmented Generation (RAG) solutions.
- Connect AI applications to enterprise documents, databases, APIs, and knowledge repositories.
- Develop retrieval and ranking strategies to provide agents with relevant context.
- Work with embeddings and vector databases.
- Implement data and context pipelines supporting AI agents.
Business Process Automation
- Analyse business processes and identify opportunities for agentic automation.
- Design AI workflows that combine LLM reasoning with deterministic business logic.
- Build agents capable of retrieving information, making decisions, invoking tools, and completing actions.
- Implement human-in-the-loop approval and escalation processes.
- Ensure automated actions are controlled, auditable, and reversible where appropriate.
Evaluation and Quality
- Develop evaluation frameworks for AI applications and agent workflows.
- Define metrics covering accuracy, task completion, reliability, latency, and cost.
- Build automated tests for prompts, agents, tools, and end-to-end workflows.
- Analyse failures and continuously improve agent behaviour.
- Use observability and evaluation data to optimise production systems.
Production Deployment
- Deploy and operate AI applications in cloud and enterprise environments.
- Implement monitoring, logging, tracing, and performance management.
- Design resilient workflows with retries, timeouts, fallbacks, and recovery mechanisms.
- Work with DevOps and platform teams to establish appropriate deployment and CI/CD practices.
Cross-Functional Collaboration
- Work closely with product managers, business analysts, software engineers, data scientists, architects, and business stakeholders.
- Communicate AI capabilities, limitations, risks, and implementation options.
- Help organisations identify realistic and valuable use cases for agentic AI.
Required Experience
- Commercial experience developing AI/LLM applications.
- Hands-on experience with LangGraph and agentic workflow development.
- Strong Python development experience.
- Experience deploying AI applications into production.
- Strong understanding of LLMs, RAG, tool calling, structured outputs, and prompt engineering.
- Experience integrating AI applications with APIs, databases, enterprise systems, or SaaS platforms.
- Experience with cloud platforms such as AWS, Azure, or GCP.
- Experience with AI evaluation, monitoring, and observability.
Desirable Skills
- LangChain / LangSmith
- Multi-agent systems
- AI workflow orchestration
- Vector databases
- Kubernetes
- Docker
- FastAPI
- Data pipelines
- MLOps
- AI security and governance
- Enterprise process automation
- Experience with financial services, healthcare, retail, manufacturing, or other complex enterprise environments
Originally posted on Himalayas
Apply for this role Opens arbeitnow.fr — the employer's own page, as linked by the source that listed this role
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Where this listing came from
- 15 Sep 2026 Arbeitnow first sighting
- 18 Sep 2026 Himalayas also listed, 2 days later
Seen on 2 boards over 2 days. The employer edited the description 8× since we first recorded it.