Senior AI Engineer

• Own end-to-end deployment of AI applications on Azure — from classical ML models and Azure OpenAI Service integrations through to production release, monitoring, and iteration.
• Build and evaluate core ML models where GenAI isn't the right tool — classification, regression, forecasting, clustering, or recommendation problems using traditional ML  techniques.
• Design and implement guardrails — content filtering, prompt injection defence, PII redaction, output validation, and human-in-the-loop checkpoints for high-risk actions.
• Build and maintain MLOps/LLMOps pipelines — CI/CD for models and prompts, feature engineering and data pipelines, automated evaluation harnesses, versioning for models/prompts/embeddings/fine-tunes, and rollback mechanisms.
• Manage the model lifecycle — model selection and routing (classical ML vs. smaller LLMs vs. frontier models by task complexity and cost), performance benchmarking, cost-per-call/cost-per-inference tracking, and deprecation/upgrade planning.
• Implement observability — logging, tracing, and alerting for LLM applications (token usage, latency, hallucination/error rates, user feedback loops).
• Architect RAG and agentic systems — vector store design, retrieval tuning, orchestration frameworks (LangGraph, Semantic Kernel, or equivalent), and multi-agent workflows where applicable.
• Collaborate cross-functionally with product owners, architects, and business stakeholders to translate requirements into scoped, deployable AI features.
• Contribute to AI governance — support responsible AI reviews, model risk assessments, and documentation required for enterprise sign-off.

Qualifications

What You Bring

Must-Have

• Minimum of 3–4 years of hands-on software/ML engineering experience, with at least 2 years specifically on GenAI/LLM applications taken to production.
• Solid grounding in core AI/ML fundamentals — supervised/unsupervised learning, model evaluation metrics, feature engineering, handling class imbalance/overfitting, and knowing when a classical ML model beats an LLM for the job.
• Hands-on experience with standard ML libraries (scikit-learn, XGBoost / LightGBM, pandas, NumPy) and at least one deep learning framework (PyTorch or TensorFlow).
• Strong working knowledge of the Azure AI/ML stack: Azure Machine Learning, Azure OpenAI Service, Azure AI Foundry, Azure AI Search, and Azure App Service/Functions for deployment.
• Practical experience with MLOps/LLMOps tooling — CI/CD pipelines, containerization (Docker), model/prompt versioning, experiment tracking, and automated testing/evaluation frameworks.
• Demonstrated experience building guardrails and safety layers in production — not just theoretical familiarity (e.g., Azure AI Content Safety, custom validation layers, jailbreak/prompt-injection mitigation).
• Solid Python engineering skills — clean, testable, production-grade code, not notebook scripts.
• Experience with at least one orchestration framework: LangGraph, Semantic Kernel, LangChain, or similar.
• Understanding of RAG architecture — chunking strategies, embedding models, vector databases, retrieval evaluation.
  • Comfort with monitoring/observability tooling (Application Insights, or equivalent) for live AI systems, including model performance monitoring and drift detection.