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
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
Job details are sourced from the employer's original posting.
