Position Type: 6-Month Contract
Location: Remote (US-Based)
Duration: 6 Months (Potential for Extension)
MUST HAVE TECH SKILL(S): Zafin Exp.************
Position Overview
We are seeking an experienced AI OS Engineer for a high-impact, 6-month contract initiative. In this role, you will lead the architecture and integration of our next-generation AI Operating System (AI OS)-a core orchestration framework designed to seamlessly manage autonomous agents, multi-LLM routing, context memory systems, tool execution, and local-to-cloud compute pipelines.
Because this is a 6-month deliverable-driven contract, you will focus on turning architectural blueprints into production-grade infrastructure, executing real-time evaluation frameworks, and optimizing latency and compute costs.
Key Responsibilities
- Design, build, and deploy agentic workflows, dynamic task schedulers, and execution runtime environments powering internal AI applications.
- Implement robust retrieval systems, long-term state persistence, vector databases (e.g., pgvector, Qdrant, Pinecone), and hybrid-search mechanisms to optimize agent context windows.
- Architect multi-model routing layers (e.g., Anthropic, OpenAI, open-source foundation models) for cost-efficiency, fallback management, and low-latency inference.
- Develop secure sandbox environments for tool execution, code generation, API calls, and agent safety protocols.
- Build evaluation harnesses to track model drift, execution accuracy, hallucination rates, and latency bottlenecks.
- Containerize and deploy AI OS infrastructure on cloud environments (AWS / Google Cloud Platform / Azure) using CI/CD pipelines.
Required Qualifications
- 5+ years of production software engineering experience, with 2+ years focused on building agentic frameworks, multi-agent orchestrations, or LLM infrastructure.
- Advanced proficiency in Python, TypeScript/Node.js, and modern async execution models.
- Hands-on expertise with agent architectures and orchestration frameworks (e.g., LangGraph, AutoGen, CrewAI, LlamaIndex, or custom in-house runtimes).
- Proven track record working with vector databases, embedding systems, and hybrid RAG implementations.
- Direct experience with Docker, Kubernetes, vLLM / Triton inference engines, and cloud platforms (AWS Sagemaker, Google Cloud Platform Vertex AI, or Azure ML).
- Mastery of RESTful/gRPC APIs, message queues (Kafka, RabbitMQ, Redis), and microservice architectures.
Preferred Qualifications
- Experience with local LLM serving, quantization methods (AWQ, GGUF), and self-hosted foundation models (Llama, Mistral).
- Deep understanding of sandboxed execution environments (e.g., WebAssembly, Docker-in-Docker, E2B) for safe AI agent tool execution.
- Prior contract experience operating in fast-paced, 6-month delivery cycles with clear milestone check-ins.
Contract Milestones & Deliverables
- Finalize system architecture, set up local/cloud runtime execution environments, and deploy the core orchestration layer.
- Integrate multi-agent tool execution, long-term memory state persistence, and guardrail protocols.
- Conduct system-wide evaluation harness benchmarking, latency/cost optimization, and handoff documentation for internal engineering teams.
