You 9ll design, build, and integrate advanced AI models into real software systems. This role sits at the intersection of software engineering, data science, and MLOps, turning research into robust, scalable, production-ready AI services.

Core Responsibilities

Model Engineering

• Build functional AI services from architectural designs
• Orchestrate data ingestion, inference flows, and output pipelines
• Optimise latency, memory, and throughput
• Implement testing, validation, and error/bias analysis

Agentic Workflows

• Design multi-agent systems using LangChain, LangGraph, or Microsoft Agent Framework
• Implement reasoning loops (e.g., ReAct)
• Integrate tools, APIs, databases, and memory systems
• Develop safety and reliability checks for agent behaviour

Data Engineering

• Build scalable ETL/ELT pipelines
• Perform feature engineering and advanced data prep
• Integrate SQL/NoSQL, data lakes, warehouses, and streaming APIs
• Ensure compliance with GDPR/CCPA and internal governance

MLOps & Deployment

• Deploy AI services on Azure using REST APIs
• Use Docker + Kubernetes for scalable production workloads
• Build ML-focused CI/CD pipelines
• Implement monitoring, drift detection, logging, alerting, and retraining
• Manage infrastructure with Terraform

Candidate Requirements

Experience

• 2+ years as an AI Engineer or Software Engineer with strong AI/ML exposure

Technical Skills

• Advanced Python (asyncio, type hinting, Pydantic)
• Backend/API development with FastAPI/Flask/Django
• Agentic frameworks (LangChain, LangGraph, Microsoft Agent Framework)
• LLM orchestration, RAG, prompt engineering
• Cloud (Azure preferred), Docker, Kubernetes
• IaC (Terraform)
• PostgreSQL, plus exposure to NoSQL and vector databases
• CI/CD, monitoring, observability, ML-specific drift detection

Collaboration & Communication

• Work effectively with data scientists, PMs, and stakeholders
• Communicate technical decisions clearly
• Maintain strong documentation across pipelines and architectures