The Senior Specialist in AI Solutions & Optimization at DP World will drive the research, design, and deployment of agentic AI systems based on meta-agent architectures and advanced Reinforcement Learning (RL). This position aims to deliver breakthrough optimizations across multi-container terminal end-to-end logistics operations.
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The role demands strategic vision, deep technical mastery, and an innovative mindset to translate cutting-edge research into real-time, large-scale solutions that enhance efficiency, resilience, and profitability while contributing to the organization’s digital transformation roadmap.
Key Responsibilities:
- Architect meta-agent frameworks that coordinate multiple autonomous agents for real-time yard, vessel, and gate optimization.
- Design, train, and fine-tune RL / RLHF / RLVR models for dynamic scheduling, resource allocation, and route planning in complex environments.
- Build end-to-end machine learning pipelines, ensuring low-latency observability for agent policies.
- Deploy and orchestrate solutions on Kubernetes-backed on-prem clusters and multi-cloud stacks (AWS, GCP).
- Enforce robust AI governance, including versioning, bias audits, and safety constraints across all interactions.
- Mentor cross-disciplinary teams in RL theory and best practices.
- Quantify ROI & KPIs and effectively communicate outcomes to executive stakeholders.
- Contribute technical foresight to ensure long-term scalability of AI initiatives.
Technical Skills Required:
The ideal candidate should possess a strong command of Python-centric AI stacks, including tools like TensorFlow, PyTorch, and Scikit-learn. Experience with open-source optimization libraries such as Pyomo, OR-Tools, and SCIP is necessary to solve complex scheduling and resource allocation problems. Proficiency in data-engineering using Apache Spark, Hadoop, Kafka, and Delta Lake is essential for enabling high-throughput data ingestion for real-time analytics.
Deployment expertise should extend across both on-prem and multi-cloud environments, employing technologies such as Docker, Kubernetes, and Terraform. Familiarity with CI/CD practices, security hardening, and performance tuning is critical. Experience with observability tools like Prometheus and Grafana, along with NLP frameworks such as SpaCy and Hugging Face Transformers, will be advantageous for automating document workflows and maritime text analytics.
Language Requirements:
Not specified.
