AI and Data Engineer

3 days ago

Remote, India ABCloudZ Full time ₹ 8,00,000 - ₹ 24,00,000 per year

Remote, India

We're looking for a skilled AI and Data Engineer to join our team. You'll be at the forefront of designing, building, and maintaining the infrastructure that powers our data-driven products and machine learning models. This role requires a strong blend of data engineering expertise, a deep understanding of MLOps principles, and the ability to work with large-scale datasets. You will be responsible for creating robust data pipelines, managing our data platforms, and collaborating with data scientists and software engineers to bring AI solutions to life.

Requirements

MSc or PhD in Computer Science, Data Science, AI/ML, Biostatistics, or a related quantitative field

2–5 years of experience developing technical solutions for automation or AI

Strong programming skills in Python and experience with Big Data applications

Hands-on experience with AWS, Azure, or GCP, including their data and ML services

Solid understanding of data lifecycle management, data architecture, and governance

Experience with CI/CD pipelines and MLOps tools

Excellent communication skills with the ability to explain complex technical concepts to non-technical stakeholders

Will be a Plus

Experience with Docker and Kubernetes for containerization and orchestration

Knowledge of real-time data processing technologies like Apache Kafka or Amazon Kinesis

Familiarity with data observability, testing, and automation frameworks

Responsibilities

Design, build, and maintain scalable data pipelines using tools like Apache Spark, Airflow, and AWS Glue

Manage and optimize cloud-based data platforms, including data lakes (S3) and data warehouses (Redshift, BigQuery)

Solve complex data integration challenges using optimal ETL patterns, sourcing from structured and unstructured data

Develop and implement MLOps frameworks to automate deployment, monitoring, and lifecycle management of ML models

Transition experimental models into production-ready code and integrate them into applications with software engineering teams

best practices in CI/CD, using tools like Jenkins, GitLab CI, and MLflow or Kubeflow

Build interactive dashboards and data products to support analytics and decision-making

Collaborate with product, QA, architecture, and engineering teams to deliver cleanly integrated data services

Support teams with robust, well-documented data models and infrastructure

Continuously monitor and improve the performance and cost-efficiency of data and AI infrastructure

Drive innovation by evaluating open-source tools and emerging technologies, and recommending solutions

Influence product and cross-functional teams to identify data opportunities that drive measurable impact

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