Data Engineer

Job Description

Job Title: Senior Data Engineer ? AI/ML & Cloud Experience: 7+ 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, Lambda Azure: Data Factory, Synapse GCP: BigQuery, Dataflow Solid 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.