
AI and Data Engineer
18 hours ago
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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