AI Engineer

Position: AI Engineer

Employment Type: Full Time

Experience: 3+ Years in AI / Machine Learning / Applied Data Science in Tech, SaaS, or Enterprise Environments

Company Description

Scry AI is a research-led enterprise AI company that builds intelligent platforms for BFSI, insurance, logistics, and industrial enterprises to drive efficiency, insight, and compliance. Our product suite includes Auriga for conversational AI, Collatio for document intelligence, and Concentio for cognitive IoT and operational intelligence. These platforms help clients convert complex, fragmented data into actionable enterprise intelligence.

We are looking for an AI Engineer to design, build, and deploy scalable AI systems that power Scry AIs enterprise platforms. This role is critical in translating advanced AI research into reliable, production-grade systems that operate securely and consistently in real-world enterprise environments.

Role Overview

As an AI Engineer at Scry AI, you will work at the intersection of machine learning, software engineering, and real-world business problems. You will be responsible for building, training, deploying, and maintaining AI models and pipelines that deliver measurable business impact across document intelligence, conversational AI, and operational intelligence use cases.

This role is ideal for someone who enjoys moving beyond experimentation and focuses on making AI systems robust, explainable, and production-ready.

Key Responsibilities

AI Model Development and Deployment

• Design, develop, and train machine learning and AI models for structured and unstructured data
• Build and deploy AI pipelines for tasks such as classification, extraction, prediction, recommendation, and anomaly detection
• Optimize models for accuracy, latency, scalability, and reliability in production environments

Data Engineering and Model Pipelines

• Work with large-scale structured and unstructured datasets, including text, documents, logs, and time-series data
• Build data preprocessing, feature engineering, and model training pipelines
• Ensure data quality, versioning, and reproducibility across experiments and deployments

Production AI and MLOps

• Deploy models into production using APIs, microservices, or batch pipelines
• Monitor model performance, drift, and reliability over time
• Collaborate with DevOps and platform teams on CI/CD, model versioning, and rollback strategies

Applied AI and System Integration

• Integrate AI models into enterprise applications, workflows, and platforms
• Work closely with product managers and engineers to translate business requirements into AI-driven solutions
• Ensure AI systems meet enterprise standards for security, privacy, and compliance

Research and Continuous Improvement

• Stay current with advances in machine learning, deep learning, and applied AI
• Experiment with new techniques, architectures, and tools to improve system performance
• Contribute to internal best practices, documentation, and AI governance initiatives

Required Qualifications and Skills

• 3+ years of hands-on experience building and deploying AI / ML systems
• Strong proficiency in Python and experience with ML frameworks such as PyTorch, TensorFlow, or similar
• Solid understanding of machine learning algorithms, deep learning, and statistical modeling
• Experience working with NLP, computer vision, time-series, or document intelligence use cases
• Familiarity with cloud platforms, APIs, containerization, and production ML workflows

Our Ideal Candidate

• Thinks in terms of production AI, not just experimentation
• Understands trade-offs between model accuracy, latency, cost, and reliability
• Is comfortable working with imperfect data and real-world constraints
• Cares deeply about explainability, robustness, and long-term system behavior
• Enjoys collaborating across research, engineering, and product teams

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