About the role
Responsibilities:
- Design scalable, reliable AI systems with a focus on production readiness, maintainability, and performance.
- Perform root cause analysis and debugging of AI pipelines to improve accuracy, consistency, and reliability.
- Develop automated evaluation, validation, and testing strategies for AI applications.
- Design reliable LLM workflows using structured outputs, validation, retrieval, and business rules.
- Create and maintain RAG pipelines and AI agent frameworks.
- Perform data analysis, model evaluation, and performance optimisation.
- Develop APIs and integrate AI solutions into existing products and platforms.
- Collaborate with cross-functional teams, including engineering and business stakeholders, to define AI use cases and deliver solutions.
- Monitor, troubleshoot, and continuously improve deployed AI systems.
Requirements:
- 3-4 years of experience building AI/ML applications with strong Python and software engineering skills.
- Strong proficiency in Python.
- Hands-on experience with: Understanding of AI system design, evaluation, monitoring, and performance optimisation.
- Experience developing, deploying, and maintaining production AI applications.
- Strong software engineering fundamentals, including clean code, modular design, debugging, and testing. o RAG architectures.
- Experience with machine learning frameworks such as TensorFlow, PyTorch, or Scikit-learn.
- Good understanding of NLP concepts.
- Familiarity with API development and integration.
- Ability to work independently as well as in collaborative team environments.
- Experience with vector databases such as Pinecone, Weaviate, FAISS, ChromaDB.
- Knowledge of cloud platforms such as AWS, Azure, or GCP. Experience with Docker and deployment pipelines.
- Exposure to fintech products, platforms, or domain-specific use cases.