#EG AI Engineer (Contact Centre)

As an AI Engineer who bridges traditional machine learning and modern generative / agentic AI. This role involves designing, building, and deploying end-to-end AI solutions including classical ML models, RAG-based applications, and lightweight agentic workflows. The successful candidate will contribute hands-on to client delivery projects, applying strong Python engineering and statistical rigor to develop scalable AI solutions that work reliably in production environments.
What will you do?
AI / ML Model Development & Deployment
Design, build, and deploy end-to-end ML models including classification, regression, time-series, and NLP models.
Integrate ML models into production systems and applications.
Monitor model performance and conduct analysis to detect data drift, quality issues, and opportunities for improvement.
Generative AI & RAG Application Development
Develop GenAI applications including RAG pipelines, prompt-engineered chatbots, and lightweight agentic workflows using frameworks such as LangChain, LlamaIndex, LangGraph, or Python.
Implement effective RAG architectures including chunking strategies, embedding selection, vector store configuration (e.g., Qdrant, Milvus, pgvector), and retrieval evaluation.
Software Engineering & Development Practices
Write clean, maintainable, and production-ready Python code.
Participate in code reviews and contribute to shared libraries and internal AI platform components.
Use Git, CI/CD practices, and containerisation tools such as Docker in development workflows.
Collaboration & Delivery
Work closely with data engineers, MLOps engineers, and client stakeholders to move AI experiments into reliable production deployments.
Document technical approaches, solution designs, and experimental results clearly.
Research & Innovation
Stay up to date with the evolving AI landscape and introduce relevant tools, techniques, and best practices to the team.
The ideal candidate should possess:
2–5 years of hands-on experience in AI/ML engineering or data science roles
Demonstrable track record of delivering at least 2–3 ML projects (beyond notebooks — production or near-production) and 1–2 GenAI / agentic AI projects
Strong Python programming skills including Pandas, NumPy, Scikit-learn, and PyTorch or TensorFlow
Solid understanding of machine learning fundamentals including feature engineering, model evaluation, bias-variance trade-off, and hyperparameter optimisation
Hands-on experience building RAG chatbots and applications using LLM APIs such as OpenAI, Anthropic, AWS Bedrock, Azure OpenAI, or equivalent
Working knowledge of RAG architecture decisions including chunking strategies, embedding model selection, vector store configuration, and retrieval quality evaluation
Practical experience with at least one vector database such as Qdrant, Milvus, pgvector, or equivalent
Experience with at least one cloud platform such as AWS, Azure, or GCP for model training or deployment
Proficiency with Git, CI/CD basics, and containerisation (Docker)
Ability to read and write at least one additional programming language such as TypeScript/JavaScript, Java, Go, or advanced SQL
Preferred Skills:
Experience building voice AI bots using a pro-code approach such as Twilio, Amazon Connect, Deepgram, Whisper + TTS pipelines
Exposure to agentic patterns such as tool-calling agents, ReAct loops, multi-step planning, or reflection mechanisms
Familiarity with MLOps tooling such as MLflow, Weights & Biases (W&B), model registries, or orchestration tools like Airflow, Prefect, or ZenML
Experience with evaluation frameworks for RAG and GenAI outputs such as RAGAS, DeepEval, or promptfoo
Knowledge of responsible AI principles including bias detection, fairness metrics, and output guardrails
Exposure to NLP pipelines including entity extraction, text classification, and summarisation
Prior experience in a systems integrator (SI), consultancy, or multi-client delivery environment

Employment type
Full-time

Work arrangement
Hybrid