Job
Full source details
✓ 92% verification score · Source: Flatrock Careers · Always confirm final requirements on the original source.
Complete source information imported
The available role or programme description, requirements, benefits and source facts were imported from the public official endpoint and formatted for reading.
EmploymentPermanent · Full Time
DepartmentSoftware
Overview
Design and develop agentic AI systems including autonomous agents, tool-using agents, multi-agent orchestration, and workflow state machines Design, build, and maintain AI-powered automation workflows Build LLM-driven agents capable of reasoning, planning, retrieving knowledge, and executing tasks across enterprise systems Integrate agents with internal APIs, CRM/ERP platforms, Jira, Confluence, Slack, email, databases, payment systems, and other business tools using function/tool calling, MCP (Model Context Protocol), and A2A patterns Develop end-to-end AI automations that combine LLM capabilities solving repetitive tasks such as document processing, lead enrichment, customer support triage, reporting, and data synchronisation across systems Connect AI agents to automation platforms via webhooks, API triggers, and custom nodes; manage scheduling, error handling, and conditional branchin
Full job description
Main Responsibilities
- Design and develop agentic AI systems including autonomous agents, tool-using agents, multi-agent orchestration, and workflow state machines
- Design, build, and maintain AI-powered automation workflows
- Build LLM-driven agents capable of reasoning, planning, retrieving knowledge, and executing tasks across enterprise systems
- Integrate agents with internal APIs, CRM/ERP platforms, Jira, Confluence, Slack, email, databases, payment systems, and other business tools using function/tool calling, MCP (Model Context Protocol), and A2A patterns
- Develop end-to-end AI automations that combine LLM capabilities solving repetitive tasks such as document processing, lead enrichment, customer support triage, reporting, and data synchronisation across systems
- Connect AI agents to automation platforms via webhooks, API triggers, and custom nodes; manage scheduling, error handling, and conditional branching within automation workflows
- Implement tool-calling schemas, input validation, error handling, retries, rate limits, and fallback logic to ensure reliable agent execution
- Design and maintain RAG pipelines using vector databases, embedding models, reranking, and chunking strategies to ground agent outputs in enterprise knowledge
- Build safety guardrails including content filters, policy constraints, tool access controls, and human-in-the-loop approval flows for high-risk actions
- Create evaluation pipelines to measure agent reliability, task success rate, accuracy, and failure-mode behaviour using tools such as LangSmith, OpenAI Evals, or custom telemetry systems
- Implement observability and tracing of reasoning steps, tool calls, latency, cost, and error rates to support debugging and continuous improvement
- Deploy and operate agent services using Docker, Kubernetes, Terraform, and CI/CD pipelines in cloud environments (AWS, Azure, or GCP)
- Monitor agent behaviour in production, diagnose anomalies, and continuously refine agent policies and performance
- Evaluate emerging agentic AI models, frameworks, and toolkits; prototype and benchmark new approaches for scalability, robustness, and safety
- Prepare technical documentation including architecture diagrams, capability descriptions, limitations, and operational guidelines
- Communicate complex AI concepts to non-technical stakeholders and collaborate across cross-functional teams to align solutions with business needs
Qualifications And Requirements
- Bachelor's or master's degree in computer science, AI, Data Science, Engineering, or a related field
- 3+ years of software engineering experience with strong proficiency in Python and/or TypeScript
- 1+ year of hands-on experience building LLM-powered applications or agentic AI systems in production or near-production settings
- Experience with agent frameworks such as LangChain, LangGraph, AutoGen, CrewAI, Semantic Kernel, or equivalents
- Hands-on experience with AI automation and workflow orchestration
- Solid understanding of LLMs, embeddings, prompt engineering, structured outputs, and function/tool calling
- Experience building and integrating REST APIs, microservices, and backend services
- Familiarity with vector databases (FAISS, Pinecone, Chroma, Weaviate) and RAG pipeline design
- Familiarity with AI-assisted development workflows (e.g., Cursor, GitHub Copilot, Claude Code) for research, architecture, and implementation
- Strong system design, debugging, and problem-solving skills
- Excellent communication skills with the ability to present technical concepts to non-technical audiences
- Experience with agent communication protocols such as MCP (Model Context Protocol) and A2A
- Experience designing AI automation solutions that combine LLMs with workflow engines for use cases such as intelligent document processing, automated reporting, chatbot backends, or AI-assisted decision support
- Experience with cloud platforms (AWS, Azure, or GCP) and cloud AI services such as Azure AI Foundry, AWS Bedrock, or Google Vertex AI
Preferred:
- Advanced experience with n8n (including custom node development and self-hosting) or Make (including advanced scenario design, iterators, and aggregators)
- Experience with evaluation and observability tools for AI agents (LangSmith, OpenAI Evals, Weights & Biases, or custom telemetry)
- Experience with reinforcement learning, planning algorithms, or multi-agent coordination
- Familiarity with model fine-tuning, RLHF, or distillation techniques
- Experience with CI/CD pipelines (GitHub Actions, GitLab CI, Jenkins)
- Experience with containerisation (Docker, Kubernetes) and infrastructure-as-code tools (Terraform, CloudFormation)
- Knowledge of security best practices: authentication, authorisation, least-privilege access, and audit logging
- Background in a regulated industry (healthcare, finance, defence, or consulting)
- Relevant certifications: AWS ML Specialty, Azure AI Engineer Associate, or GCP Professional ML Engineer
Requirements & qualifications
- Bachelor's or master's degree in computer science, AI, Data Science, Engineering, or a related field
- 3+ years of software engineering experience with strong proficiency in Python and/or TypeScript
- 1+ year of hands-on experience building LLM-powered applications or agentic AI systems in production or near-production settings
- Experience with agent frameworks such as LangChain, LangGraph, AutoGen, CrewAI, Semantic Kernel, or equivalents
- Hands-on experience with AI automation and workflow orchestration
- Solid understanding of LLMs, embeddings, prompt engineering, structured outputs, and function/tool calling
- Experience building and integrating REST APIs, microservices, and backend services
- Familiarity with vector databases (FAISS, Pinecone, Chroma, Weaviate) and RAG pipeline design
- Familiarity with AI-assisted development workflows (e.g., Cursor, GitHub Copilot, Claude Code) for research, architecture, and implementation
- Strong system design, debugging, and problem-solving skills
- Excellent communication skills with the ability to present technical concepts to non-technical audiences
- Experience with agent communication protocols such as MCP (Model Context Protocol) and A2A
- Experience designing AI automation solutions that combine LLMs with workflow engines for use cases such as intelligent document processing, automated reporting, chatbot backends, or AI-assisted decision support
- Experience with cloud platforms (AWS, Azure, or GCP) and cloud AI services such as Azure AI Foundry, AWS Bedrock, or Google Vertex AI
Preferred:
- Advanced experience with n8n (including custom node development and self-hosting) or Make (including advanced scenario design, iterators, and aggregators)
- Experience with evaluation and observability tools for AI agents (LangSmith, OpenAI Evals, Weights & Biases, or custom telemetry)
- Experience with reinforcement learning, planning algorithms, or multi-agent coordination
- Familiarity with model fine-tuning, RLHF, or distillation techniques
- Experience with CI/CD pipelines (GitHub Actions, GitLab CI, Jenkins)
- Experience with containerisation (Docker, Kubernetes) and infrastructure-as-code tools (Terraform, CloudFormation)
- Knowledge of security best practices: authentication, authorisation, least-privilege access, and audit logging
- Background in a regulated industry (healthcare, finance, defence, or consulting)
- Relevant certifications: AWS ML Specialty, Azure AI Engineer Associate, or GCP Professional ML Engineer
Tips for this job
Practical Job and Scholarship guidance. These tips do not replace official rules or create new eligibility requirements.
- Tailor the CV and application to the responsibilities and required skills stated on the official employer page.
- Use concrete evidence of relevant work, projects and measurable results rather than generic claims.
- Confirm location, work authorization, remote restrictions and sponsorship terms before applying.
- Apply through the original employer or official recruitment destination shown on this page.
Verification notes
Discovered directly from the employer’s published Personio open-positions XML feed. All public jobDescriptions sections were normalized into complete candidate-facing content.
Original authoritative source
Job and Scholarship is the discovery and verification layer. Confirm eligibility, dates, salary/funding and application instructions on the original source before submitting anything.
Flatrock Careers ↗Browse current Job and Scholarship listings from Flatrock Careers →
