Machine Learning Software Engineer

Overview

We are seeking a highly skilled Senior Python Software Engineer – AI/ML to join our team. The ideal candidate will bring deep expertise in Python and end-to-end AI/ML product delivery, with strong experience in Large Language Models (LLMs), data engineering, and MLOps frameworks. Hands-on exposure to Databricks, Data Foundry, MLflow, Prefect, and Azure ML tooling will be highly advantageous.

 

Key Responsibilities

• Design, develop, and deploy AI/ML systems into production environments.
• Orchestrate and manage scalable data pipelines with a focus on quality, reliability, and efficiency.
• Own the full AI/ML lifecycle:
• Data ingestion, preprocessing, and validation
• Model training, fine-tuning, validation, and optimization
• Deployment, monitoring, and scaling in production
• Continuous evaluation and improvement
• Implement scalable and maintainable code aligned with modern development practices (CI/CD, testing, reviews).
• Develop APIs and microservices to integrate AI/ML solutions into enterprise platforms.
• Work with containerization and orchestration technologies (Docker, Kubernetes) to enable cloud-native AI solutions.
• Collaborate cross-functionally to deliver business-focused AI capabilities.

Core Competencies & Requirements
AI/ML Expertise

• Proven experience in delivering production-grade AI/ML systems.
• Strong understanding of LLMs (fine-tuning, embeddings, integration, and deployment).
• Familiarity with NLP techniques and advanced ML workflows.

Software Engineering

• Advanced proficiency in Python for AI/ML applications.
• Skilled in scalable, maintainable code design and architecture.
• Experience with CI/CD pipelines, automated testing, and modern development best practices.

Data Engineering

• Hands-on experience with Databricks, Data Foundry, and Apache Spark for large-scale data processing.
• Ability to build and maintain ETL/ELT pipelines with strong validation and governance.
• Knowledge of pipeline orchestration frameworks such as Prefect (or Airflow).

MLOps & Frameworks

• Practical experience with MLflow for tracking, orchestration, and reproducibility.
• Strong knowledge of MLOps best practices and deployment strategies.

Cloud Platforms & Infrastructure

• Hands-on experience with Azure ML services (Data Factory, ML Studio, Synapse).
• Azure certification will be considered an advantage.
• Exposure to AWS or GCP is beneficial.
• Experience with Docker and container orchestration (Kubernetes).

API Development & Microservices

• Proven ability to design RESTful APIs and microservices.
• Experience integrating AI models into production workflows.
• Familiarity with observability best practices (metrics, logging, tracing).

Qualifications

• Bachelor’s degree in Computer Science, Computer Engineering, Software Engineering, or related field (BSc required).
• Honours or Master’s degree in a relevant field will be an advantage.
• 5+ years of professional software engineering experience, with at least 3+ years focused on AI/ML.
• Strong experience with ML frameworks such as TensorFlow, PyTorch, and Scikit-learn.
• Agile methodology experience and strong collaboration skills.