AI/ML Developer
Job Summary
Job Description
Develop and deploy AI/ML solutions for container terminal operations and large-scale logistics optimization. Responsibilities include designing end-to-end ML pipelines, optimizing algorithms for scheduling and resource allocation, researching emerging AI techniques (generative models), collaborating with cross-functional teams, building data pipelines (Spark, Hadoop, Kafka), deploying solutions on cloud and on-premises infrastructure, implementing optimization models (OR TOOLS, COIN OR, SCIP), conducting research in reinforcement learning, applying reverse engineering to enhance models, monitoring key metrics (KPIs, ROI), enforcing ethical AI usage, mentoring developers, and integrating NLP/LLMs for customer service and text analysis. The role requires expertise in machine learning (supervised, unsupervised, reinforcement learning), deep learning, NLP, generative AI (LLAMA), and experience with Docker and Kubernetes.
Must have:
- Expert in machine learning (supervised, unsupervised, reinforcement learning)
- Proficient in Python, Deep Learning, NLP
- Experience with Generative AI (LLAMA)
- Random forest algorithms
Good to have:
- Docker
- Kubernetes
15 skills required
Job Details
Hello All,
Greetings from Starkflow!
We are currently hiring for a "AI/ML Developer" for a Full Time/Permanent role in Dubai.
Must have:
Greetings from Starkflow!
We are currently hiring for a "AI/ML Developer" for a Full Time/Permanent role in Dubai.
- Job Type: Full Time
- Job Title: AI/ML Developer
- Job Location: Dubai [We are looking out from candidates who can move to Dubai]
- Years of Experience: 5 to 8 Years
Must have:
- Candidate must be very good in research, expert in machine learning- reinforcement learning,
- Supervised and unsupervised learning, Random forest algorithms, Python Generative AI- LLAMA ( Model 1 and 2), NLP and Deep Learning, Agentic AI
- Good To have - Docker and Kubernetes
- Develop multi-agent AI frameworks geared toward container terminal operations and large-scale logistics optimization.
- Design and implement end-to-end ML pipelines, including data ingestion, feature engineering, and real-time model deployment.
- Optimize machine learning and deep learning algorithms for scheduling, resource allocation, and route planning in port environments.
- Research and evaluate emerging AI techniques (e.g., generative models) to identify potential improvements in logistics workflows.
- Collaborate with cross-functional teams to integrate AI solutions seamlessly into existing operational and IT infrastructures.
- Build and maintain robust data pipelines (data lakes, streaming frameworks) using Spark, Hadoop, and Kafka for large-scale data handling.
- Deploy AI solutions on both cloud platforms (AWS, GCP) and on-premises infrastructures, ensuring reliability, security, and compliance in high-volume environments.
- Implement and fine-tune optimization models and solvers (OR TOOLS, COIN OR, SCIP etc.) for complex port planning and container yard operations.
- Conduct in-depth research and prototyping in reinforcement learning, agent-based modeling, and specialized ML techniques.
- Apply reverse engineering methods to debug, interpret, and enhance existing ML models for continuous performance gains.
- Monitor and report key metrics (KPIs, ROI) to assess the effectiveness of AI-driven solutions in real-world operations.
- Enforce ethical and transparent AI usage by adhering to governance frameworks, version control, and regulatory guidelines.
- Contribute to industry events through technical presentations, research publications, and active knowledge sharing.
- Mentor and support developers on best practices for coding, model design, and efficient use of AI libraries.
- Use lean principles to streamline development processes, reduce technical debt, and boost solution maintainability.
- Integrate NLP and LLMs for customer service, documentation automation, and large-scale text analysis in logistics.
- Collaborate on system integrations to ensure end-to-end functionality of AI modules across port operations.
- Enable real-time intelligence for vessel scheduling, yard operations, and gate control using streaming data and on-the-fly analytics.
- Maintain comprehensive domain knowledge of port and terminal terminologies to align AI solutions with operational realities.
- Contribute technical insights to the organization’s digital transformation roadmap, supporting strategic AI initiatives.
- Align AI R&D initiatives with the organization’s strategic vision, monitoring long-term impacts of AI-driven innovations, and influencing the overall digital transformation roadmap in the port and shipping sector.
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