SME - Apache Kafka, Windows PowerShell
Hyderabad · Staff/Principal
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Job description
Job Summary
Job Description: Senior Data Engineer / AI-ML Engineer (7-10 Years Experience) Position: Senior Data Engineer / AI-ML Engineer Experience: 7-10 Years Location: Any India Location / Hybrid Employment Type: Full-time Role Overview We are seeking an experienced Senior Data Engineer with strong expertise in AI/ML, Apache Kafka, and Google BigQuery to design, develop, and scale modern data platforms and machine learning solutions. The ideal candidate will have hands-on experience building real-time and batch data pipelines, implementing ML models, and enabling data-driven business decisions in a cloud-native environment.
Key Responsibilities
Design and develop scalable data ingestion, processing, and analytics solutions using Kafka and BigQuery. Build and maintain real-time streaming data pipelines for high-volume data processing. Develop, deploy, and optimize Machine Learning models for predictive analytics and intelligent automation. Collaborate with Data Scientists, Architects, Product Owners, and Business Stakeholders to translate business requirements into technical solutions. Design robust ETL/ELT frameworks and data transformation workflows. Implement data quality, governance, security, and monitoring standards. Optimize BigQuery datasets, queries, and storage for performance and cost efficiency. Establish CI/CD pipelines and automate deployment processes for data and ML workloads. Mentor junior engineers and contribute to technical leadership within the team. Drive best practices in data engineering, AI/ML, and cloud-native development.
Skill Requirements
Strong experience with Apache Kafka including Kafka Streams, Connect, and real-time event processing. Expertise in Google BigQuery for data warehousing, optimization, and advanced SQL development. Experience with Machine Learning frameworks such as TensorFlow, PyTorch, Scikit-learn, or similar. Strong programming skills in Python and SQL. Experience building ETL/ELT pipelines using modern data engineering tools. Good understanding of Data Modeling, Data Lakes, and Data Warehousing concepts. Experience with cloud platforms such as Google Cloud Platform (GCP). Knowledge of Spark, Airflow, Dataflow, Dataproc, or similar distributed processing technologies. Experience with Docker, Kubernetes, and CI/CD pipelines.
Other Requirements
Preferred Skills Experience with Generative AI, LLMs, RAG architectures, and MLOps. Knowledge of vector databases and AI model deployment frameworks. Exposure to cloud-native data architectures and microservices. Experience in financial services, banking, insurance, or large enterprise environments. Certification in GCP Data Engineering, ML Engineering, or related disciplines. Soft Skills Excellent analytical and problem-solving abilities. Strong stakeholder management and communication skills. Ability to work effectively in Agile/Scrum environments. Strong ownership mindset and leadership capabilities. Ability to mentor team members and drive technical excellence.
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