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Job Description / Roles & Responsibilities (in Detail)
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AI Engineer
Job Summary
We are seeking an innovative AI Engineer to design, develop, and deploy AI-powered applications and intelligent automation solutions. The ideal candidate will combine expertise in software engineering, machine learning, generative AI, cloud platforms, and MLOps to deliver scalable, secure, and production-ready AI solutions that drive business value.
Key Responsibilities
AI Solution Development
• Design and develop AI-powered applications and services.
• Build and deploy Generative AI solutions using LLMs.
• Develop Retrieval-Augmented Generation (RAG) systems using enterprise knowledge sources.
• Build AI agents and agentic workflows for business automation.
• Create intelligent chatbots, copilots, recommendation engines, and decision-support systems.
Machine Learning & AI
• Train, fine-tune, and evaluate machine learning models.
• Select appropriate AI models based on cost, performance, security, and business requirements.
• Implement prompt engineering and context management strategies.
• Develop model evaluation and testing frameworks.
• Monitor model performance and continuously improve AI solutions.
Engineering & Operations
• Build scalable APIs and microservices for AI inference.
• Deploy AI solutions on Azure, AWS, or Google Cloud.
• Implement MLOps and AIOps practices.
• Create CI/CD pipelines for AI applications.
• Ensure governance, security, responsible AI, and compliance standards are met.
Collaboration
• Partner with product managers, architects, and business stakeholders.
• Work with data scientists, cloud engineers, and platform teams.
• Translate business requirements into AI-enabled solutions.
Required Technical Skills
Programming Languages
• Python (mandatory)
• SQL
• JavaScript / TypeScript
• C# or Java (preferred)
AI & Machine Learning
• Machine Learning
• Deep Learning
• Natural Language Processing (NLP)
• Generative AI
• Large Language Models (LLMs)
• Prompt Engineering
• RAG Architecture
• Embeddings & Vector Search
• AI Agents / Agentic AI
Frameworks & Libraries
• PyTorch
• TensorFlow
• LangChain
• Semantic Kernel
• Hugging Face
• Scikit-learn
Cloud & DevOps
• Microsoft Azure AI Services
• Azure OpenAI Service
• Azure AI Foundry
• Azure Machine Learning
• Azure Kubernetes Service (AKS)
• Docker
• Kubernetes
• GitHub Copilot
• CI/CD Pipelines
Data & Storage
• Vector Databases
• Azure AI Search
• SQL / NoSQL Databases
• Data Engineering Fundamentals
Qualifications
• Bachelor's or Master's degree in Computer Science, AI, Data Science, Engineering, or related discipline.
• 3+ years of software engineering or AI development experience.
• Experience building and deploying production-grade AI applications.
• Knowledge of cloud-native architectures and scalable systems.
Preferred Certifications
• Microsoft Certified: Azure AI Engineer Associate (AI-102)
• Microsoft Applied Skills: Build Generative AI Solutions
• Microsoft Azure Fundamentals (AZ-900)
• Azure Data Scientist Associate (DP-100)
Key Performance Indicators (KPIs)
• AI solution adoption rate
• Model accuracy and reliability
• Response latency
• Production stability
• AI cost optimisation
• Business value delivered through AI automation
• Responsible AI compliance metrics
Modern AI Engineer Skills (2026 Focus)
Recent AI engineering guidance highlights the following as high-value capabilities:
• LLM application development
• RAG implementation
• Context engineering
• Agent design
• AI-native testing
• Evaluation frameworks
• Vector databases
• Responsible AI
• Production deployment and monitoring
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