Job Title : Senior Data Engineer – AI / ML & Cloud
Experience : 6+ Years
Location : Hybrid (1–2 days / week at any location – Noida, Gurgaon, Pune, Chennai, Bangalore, Hyderabad)
Shift Timing : 11 : 00 AM – 8 : 30 PM IST
Employment Type : Full-Time
About the Role :
We are looking for a highly skilled Senior Data Engineer with strong expertise in AI / ML and cloud data ecosystems . The ideal candidate will design, build, and optimize scalable data pipelines and intelligent analytics solutions that empower advanced data-driven decision-making. This role bridges data engineering with AI / ML deployment and MLOps in a modern cloud-first environment.
Key Responsibilities :
- Design, develop, and maintain ETL / ELT pipelines for large-scale structured and unstructured data.
- Build and optimize data models, data lakes, and warehouses on cloud platforms (AWS / Azure / GCP).
- Partner with data scientists to operationalize ML models and automate model lifecycle workflows ( MLOps ).
- Implement data validation, quality, and monitoring frameworks to ensure data reliability.
- Develop feature stores and real-time streaming solutions for AI / ML applications.
- Collaborate with business and product teams to translate data requirements into scalable engineering solutions.
- Leverage AI / ML techniques to enhance predictive analytics, data enrichment, and process automation.
- Manage and optimize data orchestration workflows using Airflow, Databricks, or Prefect.
- Ensure compliance with data governance, privacy, and security best practices.
Required Skills & Qualifications :
Bachelor’s / Master’s degree in Computer Science, Data Engineering , or a related field.7+ years of experience in data engineering with proven expertise in data pipeline and data lake design.Strong proficiency in Python, SQL, and PySpark for data processing and ML integration.Hands-on experience with cloud data services :AWS : Glue, Redshift, S3, EMR, LambdaAzure : Data Factory, SynapseGCP : BigQuery, DataflowSolid understanding of the AI / ML lifecycle , including model training, validation, and deployment.Experience with MLOps tools such as MLflow, SageMaker, Kubeflow, or Vertex AI.Proficiency in data orchestration (Airflow, Databricks Workflows) and CI / CD pipelines for data systems.Familiarity with Git , Docker , and Terraform for version control, containerization, and infrastructure automation.Strong analytical thinking, problem-solving , and communication skills.Good to Have :
Experience with Generative AI (GenAI) , LLM fine-tuning , or vector databases (Pinecone, FAISS, ChromaDB).Exposure to data observability and lineage tools (Monte Carlo, Databand, etc.).Prior experience working in Agile and DevOps environments.