Junior-Mid AI Backend Engineer; Python; CPT

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, Postgre

SQL, MySQL, No

SQL, Mongo

DB, 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, Torch Serve, 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 product ionise 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 (Postgre
  
  SQL, MySQL, or similar) including schema design, indexing strategies, query optimisation, and understanding of execution plans.
• Familiarity with No
  
  SQL or caching databases (Mongo
  
  DB, 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.