Engineer II - ML Platform

Delivery Hero Austria; Dubai; On-site; USD 70,000 - 120,000; Full time

Job summary

Delivery Hero is seeking a skilled ML Platform Engineer to enhance their machine learning capabilities. The role involves designing and maintaining scalable ML platforms, collaborating with diverse teams, and implementing robust MLOps practices. Ideal candidates will have a strong background in software engineering, generative AI, and experience with cloud infrastructures.

Qualifications

• 3+ years of experience in ML platform engineering or related roles.
• Proven experience with generative AI technologies.
• Strong software engineering background.

Responsibilities

• Design and maintain scalable ML platforms for traditional and generative AI models.
• Implement CI/CD pipelines and automate ML workflows.
• Collaborate with cross-functional teams to align ML initiatives.

Skills

Python

MLOps

Distributed Systems

Machine Learning

Generative AI

SQL

Education

Bachelor's degree in Computer Science

Advanced degree (preferred)

Tools

TensorFlow

PyTorch

MLflow

Docker

Kubernetes

Terraform

Job description

As the leading delivery platform in the region, we have a unique responsibility and opportunity to positively impact millions of customers, restaurant partners, and riders. To achieve our mission, we must scale and continuously evolve our machine learning capabilities, including cutting-edge Generative AI (genAI) initiatives. This demands robust, efficient, and scalable ML platforms that empower our teams to rapidly develop, deploy, and operate intelligent systems.

Role: ML Platform Engineer

Your mission is to design, build, and enhance the infrastructure and tooling that accelerates the development, deployment, and monitoring of traditional ML and genAI models at scale. You will collaborate closely with data scientists, ML engineers, genAI specialists, and product teams to deliver seamless ML workflows from experimentation to production, ensuring operational excellence across our ML and genAI systems.

Responsibilities

1. Design, build, and maintain scalable, reusable, and reliable ML platforms and tooling supporting the entire ML lifecycle, including data ingestion, model training, evaluation, deployment, and monitoring for both traditional and generative AI models.
2. Develop standardized ML workflows and templates using MLflow and other platforms to enable rapid experimentation and deployment cycles.
3. Implement robust CI/CD pipelines, Docker containerization, model registries, and experiment tracking to support reproducibility, scalability, and governance in ML and genAI.
4. Collaborate with genAI experts to integrate and optimize genAI technologies, including transformers, embeddings, vector databases (e.g., Pinecone, Redis, Weaviate), and real-time retrieval-augmented generation (RAG) systems.
5. Automate and streamline ML and genAI model training, inference, deployment, and versioning workflows, ensuring consistency, reliability, and adherence to industry best practices.
6. Ensure reliability, observability, and scalability of production ML and genAI workloads through comprehensive monitoring, alerting, and continuous performance evaluation.
7. Integrate infrastructure components such as real-time model serving frameworks (e.g., TensorFlow Serving, NVIDIA Triton, Seldon), Kubernetes orchestration, and cloud solutions (AWS/GCP) for robust production environments.
8. Drive infrastructure optimization for generative AI use-cases, including inference techniques (batching, caching, quantization), fine-tuning, prompt management, and model updates at scale.
9. Partner with data engineering, product, infrastructure, and genAI teams to align ML platform initiatives with broader company goals, infrastructure strategy, and innovation roadmap.
10. Contribute to internal documentation, onboarding, and training programs to promote platform adoption and continuous improvement.

Requirements

Technical Experience

1. Strong software engineering background with experience in building distributed systems or platforms designed for machine learning and AI workloads.
2. Expert-level proficiency in Python and familiarity with ML frameworks (TensorFlow, PyTorch), infrastructure tooling (MLflow, Kubeflow, Ray), and APIs (Hugging Face, OpenAI, LangChain).
3. Experience implementing modern MLOps practices, including model lifecycle management, CI/CD, Docker, Kubernetes, model registries, and infrastructure-as-code tools (Terraform, Helm).
4. Experience working with cloud infrastructure, ideally AWS or GCP, including Kubernetes clusters (GKE/EKS), serverless architectures, and managed ML services (e.g., Vertex AI, SageMaker).
5. Proven experience with generative AI technologies: transformers, embeddings, prompt engineering strategies, fine-tuning vs. prompt-tuning, vector databases, and retrieval-augmented generation (RAG) systems.
6. Experience designing and maintaining real-time inference pipelines, including integrations with feature stores, streaming data platforms (Kafka, Kinesis), and observability platforms.
7. Familiarity with SQL and data warehouse modeling; capable of managing complex data queries, joins, aggregations, and transformations.
8. Solid understanding of ML monitoring, including identifying model drift, decay, latency optimization, cost management, and scaling API-based genAI applications efficiently.

Qualifications

1. Bachelor's degree in Computer Science, Engineering, or a related field; an advanced degree is a plus.
2. 3+ years of experience in ML platform engineering, ML infrastructure, generative AI, or related roles.
3. Proven track record of building and operating ML infrastructure at scale, supporting generative AI use-cases and complex inference scenarios.
4. Strategic mindset with problem-solving skills and effective technical decision-making abilities.
5. Excellent communication and collaboration skills, with experience working cross-functionally across diverse teams and stakeholders.
6. Strong sense of ownership, accountability, pragmatism, and proactive bias for action.

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