AI Engineer

• Engage with stakeholders to understand product requirements and architect endtoend solutions incorporating data ingestion, model training, deployment and monitoring.
• Design systems that combine traditional software components with AI/ML models and modern data engineering practices.
• Write and maintain production-quality code across backend services, data pipelines and model inference layers, primarily using Python and at least one other language.
• Build RESTful or gRPC APIs to expose AI functionality and integrate with the frontend and other microservices.
• Develop and manage data pipelines for ingestion, transformation and storage (using tools such as Apache Spark, Kafka, Airflow, SQL/NoSQL databases).
• Work with vector databases and embedding stores for retrieval-augmented systems; implement embedding techniques and RAG pipelines.
• Train, finetune and deploy machinelearning and deeplearning models, including large language models (LLMs) and transformer architectures (e.g., GPT, Llama, Mistral).
• Optimise model performance (latency, throughput and accuracy) using techniques such as model quantisation, batching and caching; scale services using containerisation (Docker) and orchestration (Kubernetes) on cloud platforms (AWS, Azure, GCP).
• Collaborate with software engineers, data scientists and DevOps teams to deliver cohesive solutions; mentor junior engineers and share best practices.
• Implement observability for data pipelines and AI services (logging, metrics, model drift detection) and continuously monitor and iterate models and pipelines based on feedback and new data.

AI Architect

GenAI & AgenticAI Tech Lead

Full-Stack AI Engineer