AI​/ML Architect

About the role:

Sprint Park 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, Tensor Flow, 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.