Cloud AI & Data Engineer
Cloud AI & Data Engineer
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Job Description
Cloud Artificial Intelligence Data Engineering
Cloud Artificial Intelligence Data Engineering
6 to 10 Years
Bengaluru
Job Summary:
We are looking for an experienced Cloud AI and Data Engineer with a strong background in cloud-native data solutions AI/ML engineering and emerging Generative AI (GenAI) technologies. The ideal candidate will have 6 8 years of hands-on experience in building robust data platforms deploying scalable ML models and integrating GenAI solutions across cloud environments.
Key Responsibilities:
- Build and maintain scalable data pipelines and infrastructure for AI and analytics using cloud-native tools (e.g. AWS Glue Azure Data Factory GCP Dataflow)
- Design and implement production-ready GenAI applications using services like Amazon Bedrock Azure OpenAI or Google Vertex AI
- Develop and deploy AI/ML models including transformer-based and LLM (Large Language Model) solutions
- Integrate GenAI with enterprise workflows using APIs orchestration layers and retrieval-augmented generation (RAG) patterns
- Collaborate with data scientists product managers and platform teams to operationalize AI-driven insights and GenAI capabilities
- Build prompt engineering frameworks evaluate output quality and optimize token usage and latency for GenAI deployments
- Set up monitoring drift detection and governance mechanisms for both traditional and GenAI models
- Implement CI/CD pipelines for data and AI solutions with automated testing and rollback strategies
- Ensure cloud solutions adhere to data privacy security and regulatory compliance standards
Required Skills & Qualifications:
- 6 8 years of experience in data engineering or machine learning engineering in cloud environments (AWS Azure or GCP)
- Proficiency in Python and SQL; familiarity with PySpark Java or Scala is a plus
- Experience working with GenAI models such as GPT Claude or custom LLMs via cloud services (e.g. Bedrock Azure OpenAI HuggingFace)
- Hands-on with prompt design fine-tuning vector stores (e.g. FAISS Pinecone) and knowledge base integrations
- Experience with MLOps and LLMOps tools (e.g. MLflow LangChain SageMaker Pipelines Weights & Biases)
- Solid understanding of containerization (Docker) orchestration (Kubernetes) and microservices
- Knowledge of data lake/warehouse platforms such as S3 Snowflake BigQuery or Redshift
- Familiar with governance frameworks access control and responsible AI practices
Preferred Qualifications:
- Certifications in Cloud AI/ML platforms (e.g. AWS Certified Machine Learning Azure AI Engineer)
- Experience building RAG systems vector database search and multi-turn conversational agents
- Exposure to real-world GenAI use cases like code generation chatbots document summarization or knowledge extraction
- Knowledge of OpenAPI JSON schema validation and API lifecycle tools
Employment Type
Full Time
