GenAI Engineer - Database

We are seeking a highly skilled GenAI MLOps Engineer to join our AI Engineering team. In this role, you will design, build, deploy, and operate the core infrastructure powering our Generative AI and Machine Learning solutions. You will collaborate closely with Data Scientists, AI Engineers, Platform Engineers, and Software Development teams to productionize LLM-based applications, automate workflows, optimize infrastructure, and ensure scalable, secure, and cost-effective AI operations. The ideal candidate possesses strong expertise in cloud-native MLOps, model deployment, CI/CD automation, Kubernetes, Infrastructure-as-Code, and modern GenAI orchestration frameworks. Key Responsibilities 1. ML Pipeline Engineering & CI/CD Design, build, and maintain end-to-end ML pipelines covering: Data ingestion Data preprocessing Model training Evaluation Deployment Monitoring Develop scalable workflow orchestration using tools such as: Airflow Prefect Azure ML Pipelines SageMaker Pipelines Vertex AI Pipelines Build and maintain automated CI/CD pipelines using: GitHub Actions Azure DevOps Jenkins Automate code quality checks, security scanning, testing, model validation, and deployment processes. 2. Model Deployment & Serving Containerize AI/ML workloads using Docker. Deploy and manage ML inference workloads on: Kubernetes (AKS/EKS/GKE) Serverless platforms Cloud-native AI services Implement advanced deployment strategies including: Canary deployments Blue-Green deployments Shadow deployments A/B testing Support deployment of LLMs, RAG systems, and AI agents into production environments. 3. Monitoring, Observability & Reliability Implement observability for AI systems through logs, metrics, and distributed tracing. Monitor: Model latency Throughput Cost utilization Token consumption User traffic Service availability Create dashboards and alerting frameworks using: Prometheus Grafana Datadog Azure Monitor AWS CloudWatch Detect and resolve: Model drift Data drift Performance degradation Infrastructure incidents 4. Cloud & Infrastructure Engineering Operate and optimize AI workloads on at least one major cloud platform: Microsoft Azure AWS Google Cloud Platform Manage AI services such as: Azure Databricks Azure OpenAI AWS SageMaker Amazon Bedrock Vertex AI Build and maintain Infrastructure-as-Code using: Terraform CloudFormation ARM/Bicep Templates Provision and manage: Compute clusters Networking Storage Security controls Managed AI services 5. Generative AI Orchestration & Vector Search Build and maintain GenAI workflows using frameworks such as: LangChain LangGraph Langfuse LlamaIndex Semantic Kernel Support Retrieval-Augmented Generation (RAG) architectures. Develop and optimize: Embedding pipelines Vector database integrations Index refresh processes Knowledge retrieval systems Work with vector databases including: Pinecone Weaviate Azure AI Search OpenSearch ChromaDB FAISS 6. Security, Governance & Compliance Implement secure AI deployment practices. Manage secrets and credentials using enterprise-grade security solutions. Ensure compliance with organizational security, governance, and data privacy standards. Apply role-based access control (RBAC), encryption, and audit logging practices. Support Responsible AI and model governance initiatives. 7. Cost Optimization & Performance Engineering Monitor cloud consumption and AI infrastructure costs. Optimize: GPU utilization Compute efficiency Model serving costs Token usage Storage consumption Recommend architectural improvements that improve scalability and reduce operational expenses. 8. Cross-Functional Collaboration Partner with Data Scientists and AI Engineers to productionize models. Collaborate with Software Engineering teams to integrate AI services into products. Participate in architectural reviews and technical design discussions. Support incident management and operational excellence initiatives. 9. Documentation & Operational Excellence Create and maintain: Architecture diagram