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.
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.

Employment type
Full-time

Work arrangement
No