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Role: AI Developer / AI Engineer
Function: AI / ML / GenAI / Automation

Role Summary

The AI Developer will design, develop, integrate, and enhance AI-driven solutions for business and operational use cases. The role will focus on machine learning, GenAI, intelligent automation, data processing, and integration of AI capabilities with enterprise applications and operational platforms.

Key Responsibilities

- Design and develop AI/ML solutions based on business and operational requirements.
- Develop GenAI applications leveraging Large Language Models (LLMs).
- Build AI-powered automation for operational and business processes.
- Develop solutions for classification, prediction, anomaly detection, summarization, recommendation, and information extraction.
- Design and implement Retrieval-Augmented Generation (RAG) solutions where appropriate.
- Integrate AI models with enterprise applications through APIs and microservices.
- Develop and optimize prompts and orchestration workflows for GenAI applications.




- Evaluate AI model accuracy, quality, reliability, and performance.
- Build data preprocessing, feature-engineering, training, and inference pipelines.
- Implement appropriate controls for AI security, privacy, access, and responsible use.
- Collaborate with data analysts, developers, architects, automation teams, and business stakeholders.
- Prepare technical documentation, solution designs, and deployment artefacts.
- Monitor deployed AI solutions and continually improve their performance.

Technical Skills

- Programming: Robust Python development skills.
- AI/ML: Scikit-learn, TensorFlow, PyTorch, or equivalent frameworks.
- GenAI/LLMs: Practical experience building LLM-based applications.
- RAG: Knowledge of embeddings, vector databases, semantic search, and document retrieval.
- APIs: REST APIs, JSON, integration, and microservices.
- Databases: SQL and exposure to NoSQL databases.
- Knowledge of data processing with Pandas and NumPy.
- Experience with Git and software development lifecycle practices.
- Exposure to Azure AI, Azure OpenAI, AWS, GCP, or similar cloud AI platforms is desirable.
- Understanding of Docker, CI/CD, and deployment practices is preferred.

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