SeniorMLEngineer(ML/AI)

## Accountabilities You will be responsible for designing, building, and scaling end-to-end machine learning systems that power core AI capabilities across production environments. Design, train, fine-tune, and evaluate ML, deep learning, and generative AI models including LLMs and advanced NLP systems. Build and maintain scalable ML pipelines for training, evaluation, and real-time/batch inference. Architect and optimize data pipelines for large-scale structured and unstructured datasets, including preprocessing and vectorization. Integrate AI models into production microservices with a focus on latency, scalability, and reliability. Develop robust evaluation frameworks covering model performance, bias mitigation, alignment, and safety guardrails. Optimize models and systems for cost, latency, and efficiency using techniques such as quantization and distillation. Collaborate cross-functionally with backend, frontend, and product teams to deliver end-to-end AI features. Provide technical leadership through code reviews, architectural guidance, and engineering best practices. Mentor junior engineers and contribute to raising the overall ML engineering maturity of the team. Contribute to AI roadmap discussions and influence product direction through technical insight. Requirements: You bring strong hands-on experience in building and deploying machine learning systems in production, along with deep technical expertise in modern AI/ML architectures. 6+ years of experience building and deploying ML/AI systems in production environments. Strong proficiency in Python and experience writing production-grade code. Experience building and scaling RAG-based systems and working with vector databases. Solid understanding of deep learning, NLP, and generative AI models (including LLMs). Experience designing and maintaining scalable ML pipelines and MLOps workflows. Familiarity with microservices architecture and integration of ML models into production systems. Experience workin