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 diagrams
• Technical documentation
• Runbooks
• SOPs
• Deployment guides
• On-call support documentation

Establish best practices for AI platform operations and reliability.

Qualifications

• 5+ years of experience in DevOps, Platform Engineering, SRE, or MLOps roles.
• Minimum 3+ years supporting Machine Learning, Deep Learning, or AI production systems.
• Proficient in Databases specially Graph Db like Neo4j, memgraph (NosQL and SQL
• Must be able to do Data Modelling 
• Must know about Embeddings, Vector Database, Semantic Search
• Must have scripting and automation skills using Python, Golang, Bash, or similar languages.
• Strong hands-on expertise with one major cloud platform (Azure, AWS, or GCP).
• Experience deploying AI/ML workloads at scale.
• Strong experience with:
• Docker
• Kubernetes
• Container orchestration
• Proven expertise building CI/CD pipelines.
• Hands-on experience with Infrastructure-as-Code tools.
• Experience with monitoring and observability platforms.
• Working knowledge of:
• LLMs
• Prompt engineering
• RAG architectures
• Vector databases
• GenAI orchestration frameworks

Preferred Qualifications

• Experience working with Azure OpenAI, Amazon Bedrock, or Vertex AI.
• Hands-on experience supporting production LLM applications.
• Familiarity with GPU infrastructure and optimization.
• Experience with model evaluation frameworks and LLM observability tools.
• Knowledge of Responsible AI, AI governance, and security best practices.
• Relevant cloud certifications (Azure, AWS, or GCP) are a plus.