Engineer II - ML Platform

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 (gen AI) 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 gen AI models at scale. You will collaborate closely with data scientists, ML engineers, gen AI specialists, and product teams to deliver seamless ML workflows from experimentation to production, ensuring operational excellence across our ML and gen AI systems. Responsibilities 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. Develop standardized ML workflows and templates using MLflow and other platforms to enable rapid experimentation and deployment cycles. Implement robust CI/CD pipelines, Docker containerization, model registries, and experiment tracking to support reproducibility, scalability, and governance in ML and gen AI. Collaborate with gen AI experts to integrate and optimize gen AI technologies, including transformers, embeddings, vector databases (e.g., Pinecone, Redis, Weaviate), and real-time retrieval-augmented generation (RAG) systems. Automate and streamline ML and gen AI model training, inference, deployment, and versioning workflows, ensuring consistency, reliability, and adherence to industry best practices. Ensure reliability, observability, and scalability of production ML and gen AI workloads through comprehensive monitoring, alerting, and continuous performance evaluation. Integrate infrastructure components such as real-time model serving frameworks (e.g., Tensor Flow Serving, NVIDIA Triton, Seldon), Kubernetes orchestration, and cloud solutions (AWS/GCP) for robust production environments. Drive infrastructure optimization for generative AI use-cases, including inference techniques (batching, caching, quantization), fine-tuning, prompt management, and model updates at scale. Partner with data engineering, product, infrastructure, and gen AI teams to align ML platform initiatives with broader company goals, infrastructure strategy, and innovation roadmap. Contribute to internal documentation, onboarding, and training programs to promote platform adoption and continuous improvement. Requirements Technical Experience Strong software engineering background with experience in building distributed systems or platforms designed for machine learning and AI workloads. Expert-level proficiency in Python and familiarity with ML frameworks (Tensor Flow, Py Torch), infrastructure tooling (MLflow, Kubeflow, Ray), and APIs (Hugging Face, Open AI, Lang Chain). Experience implementing modern MLOps practices, including model lifecycle management, CI/CD, Docker, Kubernetes, model registries, and infrastructure-as-code tools (Terraform, Helm). Experience working with cloud infrastructure, ideally AWS or GCP, including Kubernetes clusters (GKE/EKS), serverless architectures, and managed ML services (e.g., Vertex AI, Sage Maker). Proven experience with generative AI technologies: transformers, embeddings, prompt engineering strategies, fine-tuning vs. prompt-tuning, vector databases, and retrieval-augmented generation (RAG) systems. Experience designing and maintaining real-time inference pipelines, including integrations with feature stores, streaming data platforms (Kafka, Kinesis), and observability platforms. Familiarity with SQL and data warehouse modeling; capable of managing complex data queries, joins, aggregations, and transformations. Solid understanding of ML monitoring, including identifying model drift, decay, latency optimization, cost management, and scaling API-based gen AI applications efficiently. Qualifications Bachelor's degree in Computer Science, Engineering, or a related field; an advanced degree is a plus. 3+ years of experience in ML platform engineering, ML infrastructure, generative AI, or related roles. Proven track record of building and operating ML infrastructure at scale, supporting generative AI use-cases and complex inference scenarios. Strategic mindset with problem-solving skills and effective technical decision-making abilities. Excellent communication and collaboration skills, with experience working cross-functionally across diverse teams and stakeholders. Strong sense of ownership, accountability, pragmatism, and proactive bias for action. Disclaimer: Naukrigulf.com is only a platform to connect jobseekers & employers. Applicants should independently verify the legitimacy of the employer. We do NOT endorse any requests for money and advise against sharing personal or bank information. For security advice, visit our website. Report any fraud to abuse@naukrigulf.com. #J-18808-Ljbffr