Staff Engineer, Machine Learning (AI)

TL;DR

Staff Engineer (Machine Learning): Responsible for converting client's business use cases and technical requirements into technical designs, defining guidelines and benchmarks, and resolving issues during code reviews. Focus on developing explainable AI models and ensuring regulatory compliance.

Company

hirify.global is a Digital Product Engineering company that builds products, services, and experiences.

What you will do

• Convert client’s business use cases and technical requirements into technical designs.
• Define guidelines and benchmarks for NFR considerations during project implementation.
• Write and review design documents, explaining overall architecture, framework, and high-level design.
• Review architecture and design on various aspects like extensibility, scalability, and security.
• Develop and design overall solutions for defined functional and non-functional requirements.
• Resolve issues raised during code reviews through systematic analysis.

Requirements

• Strong expertise in Python and ML frameworks such as TensorFlow, PyTorch, Scikit-learn, and Hugging Face.
• Solid understanding of supervised/unsupervised learning, data preprocessing, and feature engineering.
• Experience with model evaluation metrics (accuracy, precision, recall, F1-score).
• Hands-on experience with cloud ML platforms (Azure ML, AWS SageMaker, Google Vertex AI).
• Knowledge of MLOps tools like MLflow and Kubeflow for CI/CD, model lifecycle management, and monitoring.
• Strong skills in NLP and LLM engineering, including fine-tuning, prompt engineering, and RAG-based architectures.

Nice to have

• Familiarity with banking AI use cases such as fraud detection, personalization, credit scoring, and churn prediction.
• Knowledge of vector databases (FAISS, Pinecone), orchestration tools (LangChain, LlamaIndex), and conversational AI frameworks.
• Strong backend integration capabilities using REST APIs, Docker/Kubernetes, and microservices.
• Preferred certifications: TensorFlow Developer, AWS ML Specialty, Google Professional ML Engineer.

Culture & Benefits

• Dynamic and non-hierarchical work culture.