Job description

Position Overview We are seeking a Senior AI/ML Systems Engineer to help build the next generation of AI-augmented development infrastructure. This role centers on integrating and orchestrating AI technologies—both commercial and internally hosted—into our engineering workflows to improve productivity and maintain enterprise-grade quality standards. You will design and implement scalable systems that enable engineers to collaborate effectively with AI, ensuring our platforms are robust, reliable, and future-ready. This is an onsite position requiring regular in-person collaboration at our office. Responsibilities AI Systems Integration & Orchestration • Integrate AI services from APIs, self-hosted models, and open-source solutions into unified workflows. • Build abstraction layers to enable seamless switching between AI providers and models. • Design intelligent routing systems for optimal model selection based on performance, cost, and capabilities. • Develop testing and validation frameworks for AI-assisted pipelines. • Create feedback loops that improve AI tool effectiveness over time. System Architecture & Platform Development • Design scalable architectures to integrate AI across the technology stack. • Build middleware and service layers standardizing AI interactions. • Develop microservices that allow seamless AI integration. • Implement failover and resilience strategies for high availability. Full-Stack Engineering • Build backend systems in Rust for performance-critical services. • Create user-friendly interfaces for engineers to interact with AI tools. • Develop APIs using Rust frameworks (Actix-web, Axum, Rocket). • Build real-time pipelines for model serving and inference. • Create monitoring dashboards for AI system performance. AI/ML Infrastructure & Operations • Develop and maintain ML pipelines for deployment, monitoring, and lifecycle management. • Implement RAG systems and vector databases to enhance contextual AI responses. • Build infrastructure to host and serve internal AI models at scale. • Establish MLOps practices for reliable deployments and evaluations. Platform Engineering & DevOps • Containerize AI services and manage orchestration with Kubernetes. • Build CI/CD pipelines with AI-assisted testing and review. • Configure auto-scaling for inference workloads. • Manage hybrid infrastructure across cloud and on-premise. • Implement observability for distributed AI systems. Qualifications Core Requirements • 3+ years of professional experience in Rust (Tokio, async, Rust web frameworks). • 4+ years of professional experience in Python (ML frameworks, data processing, APIs). • Proven expertise in designing large-scale distributed systems. • Hands-on experience integrating commercial and self-hosted AI models into production systems. • Strong knowledge of Docker, Kubernetes, and cloud-native architectures. • Experience building APIs (REST, GraphQL, gRPC) at scale. Preferred Experience • Rust web frameworks (Actix-web, Axum, Rocket, Warp). • WebAssembly and Rust-based frontend technologies. • Vector databases (Qdrant, Pinecone, Weaviate, Milvus). • Model serving frameworks (TensorRT, Triton, vLLM). • MLOps platforms (MLflow, Weights & Biases, Kubeflow). • LLM optimization and prompt engineering techniques. Professional Background • 5-8 years of software engineering experience with at least 2 years in AI/ML systems integration. • Experience architecting enterprise systems balancing innovation, stability, and compliance. • Strong track record of deploying and maintaining production platforms at scale. • Cross-functional collaboration with data science, engineering, and product teams. • Technical leadership including mentorship, setting best practices, and driving architecture decisions. • Experience managing high-availability systems with strong SLAs. Key Focus Areas • Evaluating and selecting AI technologies for business use cases. • Developing best practices for AI-assisted development. • Building developer-friendly platforms that abstract AI complexity. • Establishing metrics to measure AI’s impact on engineering productivity. • Providing documentation, training, and guidance for adoption. Technology Stack • Languages: Rust, Python, TypeScript/JavaScript, Go • AI/ML Platforms: OpenAI, Anthropic, Google Vertex AI, AWS Bedrock, Hugging Face • Frameworks: PyTorch, TensorFlow, JAX, ONNX • Rust Ecosystem: Tokio, Actix-web, Axum, Diesel, SeaORM • Databases: PostgreSQL, Redis, MongoDB, Vector DBs • Infrastructure: Kubernetes, Docker, Terraform, AWS/GCP/Azure • Observability: Prometheus, Grafana, OpenTelemetry, Datadog Why This Role Matters AI is transforming software development. This role bridges the gap between AI capabilities and real-world engineering needs, shaping how our teams build faster, more reliable, and higher-quality systems. You’ll work on cutting-edge AI technologies while tackling large-scale engineering challenges. Location This position requires onsite presence. Candidates must be able to commute or relocate. We are committed to diversity and inclusion and encourage applications from individuals of all backgrounds.

Requirements