Cloud AI & Data Engineer

Cloud, Artificial Intelligence, Data Engineering

Skill to Evaluate :

Cloud, Artificial Intelligence, Data Engineering

Experience :

6 to 10 Years

Location :

Bengaluru

Job Description :

Job Summary:

Cloud AI and Data Engineer

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:

• 68 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