AI/ML Architect

About the role: SprintPark Solutions is looking for a

high-caliber AI/ML Architect

to lead the design and delivery of next-generation AI platforms. This role blends

technical leadership with hands-on development

in a fast-paced, innovation-driven environment. You will define architecture, build scalable AI systems, and work closely with product and engineering teams to turn emerging AI capabilities into production-grade solutions. Key responsibilities: Architect and own the

end-to-end AI/ML technical vision

across foundational models, agentic workflows, and multimodal AI systems. Design

scalable and cost-efficient AI architectures

using LLMs, RAG pipelines, fine-tuning strategies, and multi-agent orchestration. Lead evaluation and integration of frontier technologies including open-source LLMs, cloud AI platforms, and optimized inference stacks. Build and manage

robust MLOps pipelines : Data ingestion and preprocessing Feature engineering workflows Distributed model training Model deployment (Kubernetes / serverless environments) Monitoring, drift detection, and automated retraining Collaborate with product, engineering, and leadership to translate business ideas into deployable AI features. Rapidly prototype PoCs and iterate using real-world feedback. Establish Responsible AI frameworks including governance, explainability, bias mitigation, and security controls. Mentor engineers and data scientists; lead design reviews and enforce engineering best practices. Contribute hands-on development in Python and ML frameworks when required. Stay updated with emerging AI research and apply relevant innovations to maintain competitive advantage. Required Skills and qualifications: Bachelor’s/master’s degree in computer science, Artificial Intelligence, Data Science, or related field. 7+ years of experience in AI/ML engineering, architecture, or applied research roles. Strong expertise in: Machine Learning & Deep Learning LLM ecosystems and RAG architectures Model optimization and scalable inference Hands-on experience with: Python, PyTorch, TensorFlow, Scikit-learn Distributed training and data pipelines API-driven AI integrations Experience deploying ML solutions in

cloud-native environments (AWS/GCP/Azure) . Solid understanding of MLOps, CI/CD for ML, and observability practices. Ability to operate in a startup-style environment with high ownership and execution speed. Strong communication skills and ability to work with cross-functional teams. Preferred Qualifications: Experience with multi-agent systems and orchestration frameworks. Familiarity with vector databases, embeddings, and semantic search. Exposure to real-time inference optimization and cost-tuning strategies. Prior experience building AI-powered enterprise or SaaS products.