Junior-Mid AI Backend Engineer (Python) (CPT) at Datafin Recruitment

ENVIRONMENT:

• A provider of cutting-edge Tech Applications seeks the coding talents of a Junior-Mid AI Backend Engineer to join its team where you will design, build and maintain Backend services and APIs that support ML model inference, training pipelines, and Agentic AI systems.
• The ideal candidate must have 1-3 years professional experience in Backend or Software Engineering with strong Python and at least one Backend framework – Flask, FastAPI, Django or similar.
• You will also need a solid understanding of RESTful API, SQL, PostgreSQL, MySQL, NoSQL, MongoDB, Redis, Elasticsearch, Docker, Kubernetes & Git.

DUTIES:

• Design, build, and maintain Backend services and APIs that support ML model inference, training pipelines, and Agentic AI systems.
• Develop and optimise data pipelines for ingestion, transformation, and storage of structured and unstructured data used by ML workflows.
• Build and integrate model serving infrastructure (e.g., NVIDIA Triton, TorchServe, or similar) into production systems.
• Contribute to the development of orchestration layers for agentic systems, including tool-use frameworks, retrieval-augmented generation (RAG) pipelines, and multi-step AI workflows.
• Work with ML Engineers and Data Scientists to productionise models — bridging the gap between research prototypes and production-grade systems.
• Implement monitoring, logging, and alerting ML Backend services to ensure reliability and observability.
• Contribute to MLOps practices including CI/CD for model deployment, model registry integration, and experiment tracking infrastructure.
• Write clean, well-tested, and documented code following Engineering best practices.

REQUIREMENTS:

• 1–3 Years of professional experience in Backend or Software Engineering.
• Strong proficiency in Python; experience with at least one Backend framework (Flask, FastAPI, Django, or similar).
• Solid understanding of RESTful API design and development.
• Strong SQL skills — comfortable writing complex queries, working with joins, subqueries, Window functions, CTEs, and aggregations across large datasets.
• Hands-on experience with relational databases (PostgreSQL, MySQL, or similar) including schema design, indexing strategies, query optimisation, and understanding of execution plans.
• Familiarity with NoSQL or caching databases (MongoDB, Redis, Elasticsearch, etc.) and an understanding of when to use them over relational stores.
• Familiarity with containerisation and orchestration (Docker, Kubernetes basics).
• Foundational understanding of ML concepts — you should be comfortable discussing what a model does, how inference works, and what training data looks like, even if you’re not building models yourself.
• Experience with version control (Git) and collaborative development workflows.
• Good problem-solving skills and the ability to work independently in a remote environment.