Data Scientist- 5Plus Years ( Fine Tuning)

AI/ML Engineer (5+ Years)

We’re building AI systems that convert unstructured visual data into structured, usable outputs that power real-world workflows.

This role focuses on solving messy, high-variability problems and delivering reliable systems in production, not just training models in isolation.

What You’ll Do

• Build and improve end-to-end AI/ML pipelines (input ? preprocessing ? inference ? structured output)
• Work with noisy, inconsistent datasets and drive improvements in data quality and model performance
• Develop preprocessing pipelines (e.g., normalization, transformations, input standardization)
• Run experiments, tune models, and evaluate trade-offs across accuracy, latency, and cost
• Collaborate with engineering teams to productionize models (APIs, batch/real-time systems)

What We’re Looking For

• 5+ years of hands-on experience in ML/AI with exposure to computer vision
• Strong Python skills with production-quality coding practices
• Solid understanding of microservices and system design.
• Experience building and deploying ML systems beyond experimentation
• Experience working on systems that extract or process information from complex visual inputs (e.g., documents, forms, or similar structured outputs)
• Strong understanding of neural network architectures (e.g., CNNs, Transformers, sequence models) and their real-world trade-offs.
• Ability to:
• Select appropriate approaches based on problem constraints
• Fine-tune and optimize models for performance and reliability
• Debug model behavior and failure cases in production
• Strong understanding of evaluation metrics, error analysis, and model limitations

Good to Have

• Experience with cloud environments (e.g., Azure or similar)
• Familiarity with scalable data pipelines, containerization, or distributed systems
• Exposure to multi-modal systems (vision + text)

What We Don’t Want

• Only academic or demo project experience
• Over-reliance on plug-and-play APIs without understanding internals
• Inability to explain past work end-to-end (data ? model ? output ? impact)
• Profiles focused only on generic image classification or object detection use cases without real-world deployment experience