Artificial Intelligence Engineer

About the Role

Role Summary: Lead and deliver high-impact initiatives aligned to the AI & Data charter. Own execution excellence with measurable business value, technical depth, and governance.

Responsibilities

• Translate business problems into well-posed technical specifications and architectures.
• Lead design reviews, prototype quickly, and harden solutions for scale.
• Build automated Gen-AI applications and model/data governance across environments.
• Define & track KPIs: accuracy/latency/cost, adoption, and compliance readiness.
• Partner with Product, Security, Compliance, and Ops to land safe-by-default systems.

Qualifications

Technical Skills:

• Tracks: Agentic AI/GenAI/DevOps/Conversational AI/MLOps
• Python + Cloud: FastAPI, async IO; AWS/Azure/GCP basics
• Agents & RAG: LangChain/CrewAI basics, embeddings, vector DBs
• DevOps: Docker, CI, unit/integration tests, logging
• Conversational AI: intents, NLU, dialog management, evaluation
• MLOps foundations: model packaging, simple pipelines, monitoring
• LLM Deployment Use Azure OpenAI Service, AWS Bedrock, or GCP Vertex AI to deploy GPT-based or foundation models for text generation and summarization.
• LLM-Powered Bots Integrate Azure OpenAI, Bedrock, or Vertex AI with Azure Bot Service, Amazon Lex, or Dialogflow CX for advanced conversational flows.
• NLU & Intent Handling Use LUIS (Azure), Comprehend (AWS), or Dialogflow NLU for entity extraction and intent recognition.
• Speech & Language Services Implement voice interfaces using Azure Cognitive Speech, Amazon Polly, or Google Speech-to-Text.
• Context Management & RAG Ground responses with retrieval pipelines using Cognitive Search, Kendra, or Matching Engine for factual answers.
• Monitoring & Analytics Track conversation metrics and performance using Azure Monitor, AWS CloudWatch, or Google Cloud Logging.