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:

• Bachelors/masters 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.