Senior AI Engineer

1. Role Overview

We are looking for a highly hands-on Senior AI Engineer who can design and deploy real-world AI systems — including Computer Vision in factory environments, forecasting engines, real-time processing systems, and LLM-powered enterprise copilots.

This role requires strong backend engineering, ML expertise, DevOps capability, and the ability to deploy both local/on-prem models (factory environment) and cloud-based LLM solutions.

This is not a research-only role.

This is a production system builder role.

2. Key Responsibilities

A. Backend & System Architecture (Core Responsibility)

• Design and build scalable backend systems (REST APIs, microservices).
• Develop data ingestion pipelines from ERP, MRP, IoT devices, cameras, and Excel-based operational data.
• Design clean data models for production scheduling, forecasting, and factory analytics.
• Optimize performance for real-time or near-real-time processing.

B. Computer Vision (Factory Applications)

• Develop and deploy Computer Vision models for factory use cases such as:
  • Quality inspection
  • Defect detection
  • Object detection & counting
  • Production line monitoring
  • Safety monitoring
• Implement real-time inference pipelines (camera → edge model → backend → dashboard).
• Optimize models for on-prem/edge deployment (low latency, resource constraints).
• Work with OpenCV, YOLO, CNN architectures, or equivalent frameworks.
• Deploy and monitor local inference services inside factory network environments.

C. Forecasting & Advanced ML

• Develop forecasting models (demand forecasting, material planning, capacity planning).
• Build anomaly detection systems (inventory risk, constraint prediction).
• Implement time-series models (ARIMA, Prophet, LSTM, Transformer-based models).
• Translate business decision logic into ML-driven decision-support systems.

D. LLM & Cloud AI Integration

• Build enterprise AI copilots using cloud LLM services (Azure OpenAI or equivalent).
• Design RAG pipelines connecting LLMs with internal data sources.
• Implement secure API-based integration between on-prem systems and cloud AI services.
• Architect hybrid AI systems:
  • Local models for factory real-time inference
  • Cloud LLM for analytics, reasoning, and automation

E. DevOps, CI/CD & Deployment

• Containerize applications using Docker.
• Build CI/CD pipelines for AI model deployment.
• Manage multi-environment deployment (Dev / UAT / Production).
• Implement monitoring, logging, and performance tracking for AI systems.
• Ensure system reliability and security in enterprise network environments.

F. Cross-Functional Technical Ownership

• Collaborate with BA to refine and translate business requirements into technical architecture.
• Support QA in defining test scenarios for AI systems.
• Participate in UAT and production troubleshooting.
• Handle ad-hoc system issues in factory or supply chain environments.
• Take ownership from design → development → deployment → stabilization.

. Required Qualifications

• 5+ years of experience in AI/ML or backend engineering.
• Strong Python proficiency (FastAPI, Flask preferred).
• Strong knowledge of ML frameworks (PyTorch, TensorFlow, Scikit-learn).
• Hands-on experience in Computer Vision model development.
• Experience with time-series forecasting models.
• Experience deploying models to production (not only training).
• Strong SQL and database design knowledge.
• Experience with Docker and CI/CD pipelines.
• Solid understanding of system design and distributed architecture.

4. Preferred Qualifications

• Experience deploying AI systems in manufacturing or factory environments.
• Experience with edge computing or on-prem inference deployment.
• Familiarity with GPU optimization and model performance tuning.
• Experience integrating with Microsoft ecosystem (Teams, Outlook APIs).
• Experience building hybrid AI architecture (local + cloud).
• Knowledge of Azure cloud services (AI, storage, compute).

5. Soft Skills

• Strong ownership mindset and execution capability.
• Able to operate in ambiguous and evolving environments.
• System thinking — able to see end-to-end impact.
• Strong troubleshooting capability in production environments.
• Clear communication with technical and non-technical stakeholders.

6. What Success Looks Like

Within 12 months, you will have:

• Deployed real-time Computer Vision systems in factory environments.
• Built forecasting engines supporting production and supply chain decisions.
• Established CI/CD pipelines for AI deployment.
• Implemented hybrid AI architecture (on-prem + cloud LLM).
• Reduced manual operational processes through AI automation.