Build and maintain machine learning infrastructure, deployment platforms, data pipelines, APIs, CI/CD workflows, and infrastructure-as-code. The role productionizes machine learning prototypes, orchestrates structured and unstructured data workflows with Airflow and Kubeflow, automates exploratory data analysis, integrates ML capabilities into large-scale products, and performs end-to-end testing. Strong Python and GCP expertise are required, with experience in Vertex AI, PySpark, SQL, and related tooling.
This is a remote position.
We are seeking a highly skilled and motivated Machine Learning Platform Engineer to join our dynamic team. As a Machine Learning Platform Engineer, you will play a critical role in designing, building, and maintaining the infrastructure and pipelines that power our machine learning models and data processing workflows. Your expertise in Python, cloud technologies, and associated frameworks will be essential in driving the success of our machine learning initiatives.
Responsibilities:
- Deep experience in Python and associated frameworks to develop robust and efficient code for machine learning models and data pipelines.
- Build infrastructure as code using tools such as Terraform or equivalent, ensuring scalable and reliable deployment of resources.
- Design and implement CI/CD pipelines using technologies like Tekton, Jenkins, GitActions, or GCP Cloud Functions, ensuring smooth and automated software delivery.
- Develop API services to enable seamless integration of machine learning models and pipelines into various applications and platforms.
- Take MVP/PoC solutions and elevate them to production-level services and mature products.
- Conduct end-to-end testing across all stages of development to ensure first-time-right implementations.
- Manage deployment platforms for various machine learning models, ensuring modularity and efficiency.
- Integrate machine learning features into large-scale products and platforms, collaborating with cross-functional teams.
- Create and manage large-scale data transformation pipelines, enabling efficient data processing and analysis.
- Orchestrate pipelines using tools like Airflow and Kubeflow, both for structured and unstructured data workflows.
- Develop an EDA (Exploratory Data Analysis) automation framework that can be leveraged at scale for data insights.
Must-Have Skills:
- Minimum of 3+ years of hands-on experience in Google Cloud Platform (GCP) with expertise in components such as Astronomer, SQL, Pyspark, Scikit, Github, and VertexAI.
- At least 3+ years of experience in Python programming, with a strong understanding of its libraries and frameworks.
- Proven ability to convert complex requirements into practical technical solutions.
Nice-to-Have Skills:
- Familiarity with cloud technologies such as Cloud Functions, CloudBuild, and Kubernetes.
- Experience with Software as a Service (SaS) based models.
- Knowledge of deep learning frameworks such as PyTorch and TensorFlow.
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Delhi, India's capital city, is a place where tradition and progress co-exist. While Old Delhi is known for its rich history and bustling markets, New Delhi is defined by its modern architecture. It's clear the region places a strong emphasis on preserving its cultural heritage while embracing technological advancements, particularly in artificial intelligence, which plays a central role in shaping the city's tech landscape, fueled by investments in research and development.
