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.
Nice to Have
- Multi-Agent Workflows: Experience designing systems where multiple agents collaborate, negotiate, or share tasks to achieve complex goals.
- Advanced Architectures: Experience with LLM foundation model architectures or graph processing.
- AI Safety: Interest in AI evaluation, interpretability, or safety methodologies.
- Open Source: Contributions to open-source AI tooling or research-driven projects.
Why Join Us?
- Scale: Build state-of-the-art infrastructure powering applied AI at scale.
- Talent: Collaborate with a world-class team of engineers, scientists, and researchers.
- Culture: Competitive compensation with a dynamic, in-office culture in Bangalore.