AI Engineer

Key Responsibilities

• Translate CX and business needs into AI solution designs spanning NLU/LLM RAG ASR/TTS and back-end integrations.
• Define functional and non-functional requirements: latency resiliency observability security cost-per-conversation.

• Design and build agentic AI solutions single-agent multi-agent tool-using and orchestrated patterns using modern frameworks and frontier models.
• Build and optimise NLU/LLM components: classification NER summarisation RAG over enterprise knowledge bases.
• Engineer prompts tool schemas context windows and token budgets for accuracy latency and cost.
• Develop data and knowledge pipelines for ingestion cleansing PII redaction and evaluation datasets.

• Build evaluation harnesses (offline eval sets LLM-as-judge regression suites A/B testing) and treat eval as a first-class deliverable not an afterthought.
• Drive continuous improvement via conversation analytics error triage and failure-mode analysis.

• Implement CI/CD for models and prompts with feature flags canary releases and rollback.
• Track experiments lineage and model/prompt versions.
• Operate services in production against SLOs with monitoring tracing alerting and incident response.

• Apply practical guardrails for safety bias hallucination prompt-injection and jailbreak resistance.
• Enforce GDPR / LOPDGDD: consent minimisation retention access control auditability.

• Work alongside conversation designers software engineers data scientists QA and Ops.
• Produce clear design docs runbooks and stakeholder updates.

Skills Knowledge and Expertise

• Hands-on experience delivering conversational AI voicebots chatbots or virtual assistants including conversational flows open-ended interactions NLU and generative AI.
• Production experience building agents with generative AI: agentic architectures (single-agent multi-agent tool/function-calling plannerexecutor patterns) RAG and orchestration.
• Working knowledge of frontier LLM providers and platforms e.g. Anthropic Claude OpenAI Google Gemini Amazon Bedrock Microsoft Azure AI and a practical sense of when to use which.
• Strong prompt engineering and context engineering skills across multiple LLM families including system prompt design structured output and grounding strategies.
• An evaluation mindset: you measure agent quality with eval sets traces and metrics not vibes.
• Awareness of AI ethics bias and safety with practical experience mitigating them in deployed systems.
• Token latency and cost optimisation model routing caching (incl. prompt caching) context compression retrieval tuning as a core engineering discipline.
• Comfort with agentic development workflows using AI coding assistants and AI co-work / pair-development models (Claude Code Copilot Cursor or equivalent) as part of your day-to-day delivery.
• Solid software engineering fundamentals: Python and/or JavaScript/Node Git IDEs Agile delivery.
• Customer-facing skills: requirements gathering design validation and stakeholder communication.
• Working proficiency in Spanish and English.

• Cloud services primarily AWS with containerised deployment via Docker / Kubernetes.
• Contact centre integrations (e.g. Genesys Avaya Cisco Amazon Connect) and Meta / WhatsApp Business / digital channel integrations.
• Experience with ASR/TTS and voice-specific concerns: barge-in turn-taking ASR error recovery latency budgets.
• Speech/NLU recognition models and continuous-improvement loops (Microsoft STT MS CLU Google Amazon).
• Observability for LLM systems: tracing eval pipelines online quality monitoring.
• Familiarity with emerging agent interoperability standards (e.g. MCP A2A) and human-in-the-loop / escalation patterns.
• Microsoft Bot Framework Genesys Dialog Engine or VXML exposure.
• Consulting experience analysing existing bot estates evaluating KPIs (containment CSAT AHT transfer rate) and recommending improvements.

• Exposure to Google Conversational Agent / Playbooks or DialogFlow useful if youve worked with it but not required.

Benefits