BioQuest Solutions

Description :The AI Solution Architect will lead the design, development, and deployment of scalable AI solutions across the organization. The role involves defining AI architecture, guiding a high-performing AI engineering team, and ensuring seamless integration of AI capabilities into existing systems. The ideal candidate brings strong AI/ML expertise, solution architecture experience, and leadership skills.Key Responsibilities :- Lead and manage the AI development team to deliver scalable, production-ready AI solutions.- Conduct research to identify the best AI models, frameworks, and technologies for integration into the company ecosystem.- Develop and oversee Proofs of Concept (PoCs) to validate AI-driven capabilities and business value.- Collaborate with stakeholders to align AI initiatives with product vision and business strategy.- Drive recruitment, onboarding, mentoring, and continuous development of AI and engineering talent.- Oversee automated, manual, regression, progression, and exploratory testing efforts and provide deployment validation support.- Define the AI architecture and technical roadmap ensuring smooth integration into cloud platforms and existing systems.- Own the end-to-end lifecycle of AI feature developmentfrom ideation and design to deployment and performance tuning.- Establish MLOps practices for continuous training, evaluation, deployment, and monitoring of AI models.- Ensure compliance with data privacy regulations, ethical AI guidelines, and internal security standards.Technical Skills Required :- Proficient in Python and frameworks such as Django REST Framework.- Strong command of AI/ML libraries including NumPy, scikit-learn, TensorFlow, PyTorch, Torch, Sentence-Transformers, SpaCy, and NLTK.- Experience in prompt engineering and working with AI-powered NLP tools.- Familiarity with Generative AI (GenAI) approaches and their practical implementation.- Experience with end-to-end model development, deployment, and performance optimization in cloud environments.- Strong understanding of MLOps processes including CI/CD for ML, model monitoring, and continuous training pipelines. (ref:hirist.tech)