AI & Data – AI Engineer

About the Role

The Senior AI Engineer designs, builds, and ships enterprise-grade AI/ML and LLM-based solutions. This role focuses on hands-on engineering, high-quality delivery, and strong collaboration with cross-functional teams.

Key Responsibilities

• Design, build, and deploy AI/ML and LLM-based solutions in enterprise environments.
• Collaborate with cross-functional teams (Data Engineering, Cloud, Product) to deliver scalable AI systems.
• Ensure high engineering standards, maintainability, and best practices.
• Participate in code reviews, architecture discussions, and solution design.
• Support continuous improvement of AI delivery processes and tooling.

Skills & Qualifications

Python & Development

• Advanced Python (3–6 years)
• FastAPI
• scikit-learn
• API design
• clean code
• Preferred: intermediate SQL, Design patterns (clean architecture/hexagonal); microservices; advanced testing; Docker
• What we evaluate: Code quality; API design; troubleshooting; software architecture discipline; applied SQL

LLMs, RAG & Agents:

• End-to-end RAG
• LangChain/LangGraph
• Vector search (FAISS or similar)
• Fine-tuning (LoRA/QLoRA)
• Advanced evaluation (RAGAS/TruLens/DeepEval)
• Agent design
• Autogen
• Preferred: Llama Index; custom retrievers
• What we evaluate: Hallucination mitigation; grounding; cost/latency trade-offs; quality

Cloud (Azure or Databricks):

• Cloud (Azure): Azure OpenAI; Azure AI Search; Azure ML; service integration; AKS/Container Apps; API Management
• Databricks: Advanced MLflow (registry/tracking/serving); Delta Lake; Unity Catalog; Feature Store; Vector Search
• Preferred: Workflows/DLT
• What we evaluate: Secure & scalable architectures; integration; resilience, Pipelines; governance (Unity Catalog); productivity

MLOps & Delivery:

• CI/CD (GitHub Actions/Azure DevOps)
• Docker
• AKS/Kubernetes
• End-to-end ML pipelines
• Basic monitoring (latency, cost, failures)
• Preferred: AI observability (tracing/telemetry); advanced Bicep/Terraform
• What we evaluate: Reliability; diagnostics; automation

ML Fundamentals:

• Classic models
• Advanced metrics & trade-offs
• When to use classic ML vs. LLMs
• Preferred: Advanced/ensemble models
• What we evaluate: Technical judgment; model validation

Communication and other requirements:

• English: Fluent B2+ technical communication
• Autonomy in English, Technical clarity
• Proactive
• Good at managing request gathering and handling
• Proactive communication