Machine Learning Engineer

Must have - 3–4 years of experience building AI/ML applications with strong Python and software engineering skills.

• Strong proficiency in Python.
• Hands-on experience with: o Understanding of AI system design, evaluation, monitoring, and performance optimization. o Experience developing, deploying, and maintaining production AI applications. o 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. Good to have - 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.

Roles & 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 optimization.
• 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.