Senior Data Engineer – AI/ML & Cloud

About the Role:

exp: 5+ years

We are seeking a Senior Data Engineer with AI/ML expertise to design, build, and optimize scalable data pipelines and intelligent analytics solutions. The ideal candidate will combine strong data engineering capabilities with hands-on experience in AI/ML model development, deployment, and MLOps within a modern cloud ecosystem.

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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 using modern cloud platforms (AWS/Azure/GCP).
• Collaborate with data scientists to operationalize ML models and automate model training and deployment (MLOps).
• Implement data quality, validation, and monitoring frameworks to ensure reliable pipelines.
• Develop feature stores and real-time data streaming solutions for AI/ML use cases.
• Work with business and product teams to understand data requirements and translate them into scalable engineering solutions.
• Use AI/ML techniques to enhance data enrichment, predictive analytics, and process automation.
• Manage and optimize data orchestration workflows using tools like Airflow, Databricks, or Prefect.
• Ensure compliance with data governance, privacy, and security policies.

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Required Skills & Qualifications:

• Bachelor’s/master’s degree in computer science, Data Engineering, or a related field.
• 7+ years of experience in data engineering, including data pipeline and data lake design.
• Strong proficiency in Python, SQL, and Pyspark for data processing and ML integration.
• Hands-on experience with cloud services – AWS (Glue, Redshift, S3, EMR, Lambda), Azure (Data Factory, Synapse), or GCP (Big Query, Dataflow).
• Solid understanding of AI/ML lifecycle, including model training, validation, and deployment.
• Experience with MLOps tools such as MLflow, Sage Maker, Kubeflow, or Vertex AI.
• Knowledge of data orchestration (Airflow, Databricks Workflows) and CI/CD pipelines for data systems.
• Familiarity with version control (Git), containerization (Docker), and infrastructure-as-code (Terraform).
• Strong analytical, problem-solving, and communication skills.

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Good to Have:

• Experience with GenAI, LLM fine-tuning, or vector databases (Pinecone, FAISS, Chroma DB).
• Exposure to data observability and lineage tools (Monte Carlo, Data band, etc.).
• Prior experience working in Agile / DevOps environments.