Build the Systems Behind the Next Generation of Businesses.
We deploy production-grade AI systems, intelligent workflows, and resilient enterprise architectures. We are looking for exceptional engineers who prioritize operational precision over software bloat.
GenAI & Intelligent Systems Engineer
Role Overview: The AI/ML Engineer designs, develops, and deploys production-grade AI systems across Generative AI, machine learning, and intelligent applications. You will take AI systems from data preparation through model evaluation, integration, and production monitoring.
Key Responsibilities:
- Design and develop AI/ML solutions for real business workflows.
- Develop Generative AI applications using LLMs and multimodal models.
- Build RAG-based applications, vector search pipelines, and agentic workflows.
- Develop model evaluation, validation, and guardrail frameworks to eliminate hallucination risk.
- Deploy AI applications across cloud environments with telemetry logging and observability.
Required Skills & Tools:
Cloud & Infrastructure Platform Engineer
Role Overview: Architect and manage high-availability cloud infrastructure, Kubernetes clusters, model serving gateways, and automated CI/CD deployment pipelines for enterprise client environments.
Key Responsibilities:
- Provision Infrastructure-as-Code (IaC) using Terraform across AWS, GCP, and Azure.
- Configure Kubernetes (EKS/GKE) clusters for scalable model serving and vLLM deployments.
- Establish observability, monitoring, logging, and token cost-control gateways.
- Enforce zero-trust security controls, network isolation, and KMS encryption.
Required Skills & Tools:
High-Throughput Microservices & RAG Engineer
Role Overview: Construct resilient backend microservices, async message queues, database schema designs, and REST/gRPC API gateways connecting legacy enterprise systems with modern AI capabilities.
Key Responsibilities:
- Develop high-performance REST and gRPC endpoints using Python (FastAPI) or Node.js.
- Architect async workflow processing pipelines with Redis, Celery, or RabbitMQ.
- Integrate enterprise ERPs, CRMs, and SQL/NoSQL databases with AI processing services.
- Implement strict RBAC authorization, token authentication, and audit logging.