Teams at FactEntry

Translates business needs into technical requirements, BRDs, FRDs, user stories, workflows, and roadmaps. Manages JIRA boards, backlogs, prioritization, sprint tracking, and reporting. Acts as liaison between stakeholders and development teams, conducts functional testing and UAT support, tracks defects, and facilitates walkthroughs, sprint reviews, and status updates. Identifies delivery risks, dependencies, and blockers while ensuring solutions meet documented business requirements.

Build and deploy classical machine learning and generative AI solutions, including regression, classification, clustering, LLM, RAG, and prompt-engineering applications. Develop real-time APIs, integrate vector databases, optimize inference, monitor production models, and promote safe, explainable, unbiased AI outputs. Collaborate with business teams to translate requirements into ML use cases. Required expertise includes Python, common data science libraries, LLM frameworks, transformer architecture, NLP, REST APIs, cloud platforms, Docker, and Kubernetes.

Lead development of web applications using Django and React.js. Collaborate with stakeholders on requirements and project scope, mentor engineers, enforce coding standards, optimize performance, troubleshoot issues, and coordinate with QA and DevOps for integration and deployment. The role requires strong software development expertise, agile leadership, communication, problem-solving, and project prioritization skills.