Role : Principal/ Senior AI Engineer

Department : Engineering

About Nasiko :

Nasiko is building the intelligent coordination layer for AI agents a system that enables autonomous agents, data services, and infrastructure components to discover, register, and interact seamlessly. Our mission is to create an open, self-organizing AI ecosystem where agents can reason, collaborate, and transact without centralized control or human micromanagement. At the heart of this vision lies the Nasiko Registry : a shared substrate for observability, execution, and interoperability across distributed AI systems. We operate at the intersection of multi-agent intelligence, system orchestration, and adaptive infrastructure building the primitives that make AI systems composable, transparent, and intelligent by design.

Role Overview :

As an AI Engineer at Nasiko, you will design, build, and optimize high-performance backend systems and agentic frameworks that power our distributed AI ecosystem. You will collaborate closely with ML engineers and platform teams to deploy AI-powered services, scale intelligent workflows, and serve models efficiently across diverse environments. Your work will be central to enabling robust, interoperable, and adaptive AI coordination.

Core Responsibilities :

- System Architecture : Design and maintain robust backend systems, microservices, and APIs using Python, focusing on high-level system design and architecture.

- Distributed Infrastructure : Architect and deploy distributed infrastructure for high-throughput AI workloads and agent coordination.

- Model Integration : Collaborate with ML engineers to integrate trained models into scalable production environments.

- Inference Pipelines : Build and maintain real-time and batch inference pipelines for AI-driven tasks.

- MLOps & DevOps : Contribute to CI/CD automation, observability, monitoring, and autoscaling for AI services.

- Performance Optimization : Ensure high availability, security, and performance across all backend deployments and model-serving infrastructure.

Skills and Qualifications (Must Have) :

- Strong professional experience with Python in production-grade systems.

- Deep understanding of system design, distributed systems, Python asyncio models, and network programming.

- Proven experience building AI Agents using frameworks like LangChain or OpenCLAW.

- Hands-on experience with MCP (Model Context Protocol) servers : understanding what they are, how to build them to provide tools/data to LLMs, and how they facilitate agentic communication.

- Expertise in Vector Databases (e.g., FAISS, Pinecone, Weaviate) for building Knowledge Bases and RAG (Retrieval-Augmented Generation) pipelines.

- Familiarity with ML frameworks such as PyTorch, TensorFlow, or JAX.

- Experience with model-serving platforms like Triton Inference Server, TorchServe, ONNX Runtime, or Ray Serve.

- Proficiency in containerization and orchestration using Docker and Kubernetes.

- Experience with SQL/NoSQL databases, caching systems (Redis), and message queues like Kafka or RabbitMQ.

- Cloud platform proficiency (AWS, GCP, or Azure) and experience with infrastructure-as-code.

- Proficiency in Git and modern GitHub/GitLab workflows.