About the role
Responsibilities & Required Skills
Core Engineering Strong hands-on experience with Python, including backend development using Django, following clean architecture and production-quality engineering practices. Solid understanding of RESTAPI design, databases, and cloud-native application architecture. Strong debugging, systems-thinking, and problem-solving skills. Cloud & Infrastructure Proven experience building and deploying AI applications on both AWS and Azure — these are our primary cloud platforms, so depth in both matters. AWS: practical knowledge of core services required to build AI use cases end-to-end (compute, storage, databases, networking, IAM, monitoring), plus direct experience with AWS Bedrock (foundation model access) and AWS AgentCore (or equivalent AWS agent-building services).
Azure:
practical knowledge ofAzure services forAI/ML workloads (e.g., Azure OpenAI Service, Azure AI Studio, Azure Functions, Azure networking/IAM equivalents). Working knowledge of GCP forAI/ML workloads (e.g., Vertex AI, Cloud Functions, Cloud Run) is a plus. Comfort building custom, self-hosted AI solutions when a use case doesn't fit neatly into a managed cloud service (e.g., self-hosted open-source models, custom inference pipelines, containerized deployments). Experience setting up and maintaining deployment/CI-CD pipelines forAI applications (containerization, automated testing, model/version rollout, environment management) across cloud platforms.
LLM Orchestration & Agentic Framework
s Demonstrated experience orchestrating multiple LLMs within a single application (routing between models, multi-agent coordination, chaining, tool use). Practical experience with LangGraph and/or LangChain, plus hands-on exposure to at least one other agentic framework such as CrewAI, AutoGen, LlamaIndex Agents, or Semantic Kernel. Experience using Langfuse (or similar tools) for LLM observability, tracing, and evaluation.