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