Skills

Full job description

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