About the role
We are looking for a strong Machine Learning / AI Engineer with 4-6 years of experience who can build and deploy production-grade ML and Generative AI systems. The ideal candidate combines strong software engineering fundamentals with hands-on experience in ML, LLM applications, RAG, computer vision, MLOps and cloud-based distributed systems.
Responsibilities:
- Design, develop and deploy scalable, production-grade machine learning and AI systems.
- Build Generative AI and LLM-based applications, including retrieval-augmented generation (RAG) systems.
- Identify reusable patterns and create modular AI/ML solutions and engineering assets.
- Work across AI/ML, distributed systems and product engineering to solve complex business problems.
- Evaluate and integrate models and APIs across OpenAI, Anthropic and Google Gemini ecosystems.
- Read, understand and implement relevant research papers and emerging ML/AI techniques.
- Contribute to technical direction, architecture and engineering best practices.
- Mentor engineers and demonstrate strong ownership with a bias toward action and execution.
Requirements:
- Bachelor's degree in Computer Science, Engineering or a related technical field; Master's or PhD preferred.
- 4-6 years of software engineering experience with significant exposure to machine learning systems.
- Strong proficiency in Python and solid computer science fundamentals, including data structures, algorithms and system design.
- Proven experience building and deploying production-grade ML systems at scale.
- Deep understanding of machine learning concepts including optimisation, statistics and linear algebra.
- Hands-on experience with Generative AI / LLM-based systems.
- Experience with RAG architectures, vector databases, embeddings and retrieval systems.
- Hands-on experience with PyTorch and/or TensorFlow/Keras.
- Experience with MLOps practices including ML CI/CD, model versioning, monitoring and drift detection.
- Experience with AWS, GCP or Azure and distributed computing frameworks such as Spark or Ray.
- Ability to write clean, modular and reusable code.
Good to Have:
- Experience with computer vision techniques such as object detection and semantic or instance segmentation.
- Experience evaluating models across OpenAI, Anthropic and Gemini ecosystems.
- Contributions to open-source projects, research publications or patents.
- Strong experience reading, implementing or adapting research papers.
- Experience influencing technical direction and mentoring engineers.