Agentic AI Engineer

• 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 • 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