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Company Overview
A global technology, defence, and engineering organization with a presence across Asia, Europe, the Middle East, and the U.S., serving clients in over 100 countries. The organization leverages technology and innovation to solve real-world problems across aerospace, smart cities, defence, and public security. With deep domain expertise in cybersecurity, it provides robust solutions in cryptography, digital authentication, SCADA protection, security operations centers, and managed security services to strengthen the resilience of government, critical infrastructure, and enterprise clients.
Summary
The Machine Learning Engineer will design, build, and deploy agentic AI models capable of autonomous decision-making and adaptive behavior. This role requires bridging experimental AI models with production-grade ML systems, translating complex data and simulations into decision-capable AI solutions. The ideal candidate combines technical excellence with research-savvy insights to advance intelligent agent behavior in real-world and digital twin environments.
Responsibilities
Design and implement agentic AI models for autonomous decision-making, planning, and tool use in complex environments
Develop and train machine learning models for both simulation-based (digital twin) testing and live production deployment
Translate high-level behavioral specifications into scalable, modular ML architectures for adaptive agents
Collaborate with data scientists, AI architects, and systems engineers to integrate models into AI pipelines and system architectures
Optimize models for efficiency, robustness, and real-time performance in distributed and resource-constrained environments
Implement continuous training, fine-tuning, and evaluation workflows to support evolving agent behavior
Ensure model outputs align with system goals, safety constraints, and operational performance metrics
Contribute to MLOps pipelines, including model versioning, deployment automation, monitoring, and rollback mechanisms
Requirements
Experience
5+ years of experience in machine learning engineering or applied AI, including production deployment
Demonstrated success developing and deploying ML models in simulation and real-time operational contexts
Technical Skills
Proficiency in ML frameworks such as PyTorch and TensorFlow
Experience with agentic and reinforcement learning tools (e.g., Ray RLlib, Stable-Baselines)
Strong model training, evaluation, and deployment experience for digital twins and live systems
Familiarity with MLOps, CI/CD pipelines, and monitoring for model performance and drift
Understanding of agent-based modeling, planning algorithms, and state/action representations
Experience with containerization (Docker), orchestration (Kubernetes), and cloud platforms (AWS, GCP, Azure)
Preferred Knowledge
Exposure to agentic AI systems, multi-agent coordination, or intelligent automation frameworks
Familiarity with digital twin architectures, synthetic data generation, and sim-to-real transfer techniques
Experience integrating models into service-oriented or microservice architectures
Disclaimer
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1. No Endorsement: Job ads on SpringCube do not imply endorsement of their authenticity or quality. 2. No Client Relationship: This company is not a client of SpringCube unless stated. 3. To Apply: Click the “Apply” button to be redirected to the hiring company’s application page for this job. 4. No Liability: SpringCube is not liable for inaccuracies.
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