Pragmatike

Full-stack Engineer

• Build, deploy, and operate production infrastructure across AWS, GCP, and Azure.
• Maintain consistent environments using Infrastructure as Code (Terraform preferred).
• Deploy and operate Kubernetes clusters and containerized workloads across multiple cloud providers.
• Design and manage cloud networking (VPC/VNet design, peering, load balancing, private connectivity).
• Implement monitoring, logging, alerting, and incident response for multicloud systems.
• Optimize performance, reliability, and cost across providers through autoscaling and capacity planning.
• Support AI training and inference workloads in multicloud environments.
• Troubleshoot complex production issues spanning compute, networking, storage, and Kubernetes layers.
• Collaborate closely with AI, backend, and platform teams to support production systems.

Senior Engineer – Multicloud

DevOps Engineer

• Design and maintain Kubernetes clusters across multiple environments using Helm, KEDA, and HPA for auto-scaling
• Architect and optimize PostgreSQL databases with schema design, migrations via Drizzle ORM, and performance tuning
• Build and maintain Docker containerization with multi-stage builds and container optimization strategies
• Implement cloud infrastructure solutions on Scaleway and AWS-equivalent services (S3, SQS patterns)
• Configure and troubleshoot Linux systems running X11/Xvfb, PulseAudio, FFmpeg, and v4l2loopback for bot operations
• Develop CI/CD pipelines using GitHub Actions and container registry workflows
• Monitor system performance using Grafana, Promtail, and implement structured logging across services
• Collaborate with the technical team to scale meeting bot infrastructure and real-time processing systems

Fullstack/DevOps Engineer

Machine Learning Engineer

• Architect, build, and scale the end-to-end ML Ops pipeline, including training, fine-tuning, evaluation, rollout, and monitoring.
• Design reliable infrastructure for model deployment, versioning, reproducibility, and orchestration across cloud and on-prem GPU clusters.
• Optimize compute usage across distributed systems (Kubernetes, autoscaling, caching, GPU allocation, checkpointing workflows).
• Lead the implementation of observability for ML systems (monitor drift, performance, throughput, reliability, cost).
• Build automated workflows for dataset curation, labeling, feature pipelines, evaluation, and CI/CD for ML models.
• Collaborate with researchers to productionize models and accelerate training/inference pipelines.
• Establish ML Ops best practices, internal standards, and cross-team tooling.
• Mentor engineers and influence architectural direction across the entire AI platform.

Principal ML Ops Engineer

AI Engineer

• Own the end-to-end architecture and technical development of the Graph AI platform
• Improve and scale the private relationship graph (matching, pathfinding, performance, and privacy)
• Design and implement AI-powered relationship scoring and recommendation systems
• Develop Graph RAG and intelligent introduction recommendations
• Build and ship core backend and infrastructure features
• Maintain and improve data pipelines (email, LinkedIn, CRM integrations, identity resolution)
• Optimize onboarding and ensure fast product “aha moment”
• Lead cloud infrastructure and DevOps (AWS, Docker, deployments, scaling, reliability)
• Ensure performance, scalability, and security of the system
• Make key decisions on system design, architecture, and AI implementation
• Collaborate closely with founders on product roadmap and technical strategy
• Contribute to building a strong and defensible network-effect-driven platform

AI Engineer – Tech Lead

Backend Engineer

• Design and develop scalable, distributed, server-side applications and microservices using Go.
• Build and optimize mission-critical backend software, with a strong focus on system programming, concurrency, and performance tuning.
• Design and implement robust peer-to-peer (P2P) communication systems (P2P experience is mandatory).
• Contribute to or build systems using libp2p or similar P2P frameworks.
• Ensure cross-platform compatibility and consistent application behavior across environments.
• Collaborate within an Agile/Scrum team, contributing to backlog refinement, risk identification, and dependency management.
• Produce clean, maintainable, and reusable code, following strong engineering standards.
• Optimize backend systems for performance, reliability, security, and fault tolerance.
• Establish and enhance backend infrastructure, including deployment pipelines, observability, and monitoring tooling.
• Work across multi-cloud environments (AWS, Azure, GCP) and collaborate closely with infrastructure teams.
• Participate actively in the full application lifecycle, from design to production operations.
• Engage with product and design teams to deeply understand end-user and platform requirements.
• Conduct thorough code reviews, mentor other engineers, and raise overall backend engineering quality.
• Champion best engineering practices and a culture of high-quality software craftsmanship.

Principal Backend Engineer, Golang

• Design, build, train, and optimize machine learning and deep learning models for production use
• Lead the deployment of ML models into scalable, reliable production systems
• Architect training, inference, and evaluation pipelines for structured and unstructured data
• Own model performance, reliability, scalability, and lifecycle management
• Drive experimentation, model iteration, and performance benchmarking
• Implement and maintain high-quality ML codebases primarily in Python
• Monitor, debug, and improve live production models
• Collaborate with platform, backend, and infrastructure teams on system integration
• Contribute to technical direction, architecture decisions, and AI roadmap planning
• Mentor junior engineers and support team-level technical excellence

Principal AI/ML Engineer

Engineer

• Design, implement, and optimize custom CUDA kernels for NVIDIA GPUs, with a focus on maximizing occupancy, memory throughput, and warp efficiency.
• Profile GPU workloads using tools such as Nsight Compute, Nsight Systems, nvprof, and CUDA‐MEMCHECK.
• Analyze and eliminate performance bottlenecks including warp divergence, uncoalesced memory access, register pressure, and PCIe transfer overhead.
• Improve GPU memory pipelines (global, shared, L2, texture memory) and ensure proper memory coalescing.
• Collaborate closely with AI systems, model acceleration, and backend distributed systems teams.
• Contribute to GPU architecture decisions, kernel libraries, and internal performance-engineering best practices.

CUDA Kernel Engineer

Staff/Principal ML Ops Engineer

Artificial Intelligence

• Design, build, and maintain scalable full stack applications using Node.js, React, and TypeScript.
• Architect and develop robust backend services, APIs, and data models.
• Build modern, production-grade web interfaces with React.
• Own features end to end, from technical design and implementation to deployment and production support.
• Optimize performance across the stack (frontend rendering, API latency, database queries).
• Collaborate closely with product, design, and AI/ML teams to ship user-facing features.
• Establish best practices around testing, code quality, observability, and CI/CD.
• Improve overall system architecture, scalability, and developer experience.
• Debug and resolve production issues across both frontend and backend systems.

Principal FS Engineer, AI

Cloud Engineer

Senior Cloud Engineer

Frontend Engineer

• Build and maintain modern, production-grade web interfaces using React and TypeScript.
• Translate product requirements and design systems into clean, scalable UI components.
• Own complex frontend features end to end, from implementation to production support.
• Optimize UI performance for large datasets, real-time updates, and high-usage dashboards.
• Collaborate closely with product, design, and backend teams to ship user-facing features.
• Establish frontend best practices around state management, testing, and code quality.
• Improve frontend architecture, tooling, and developer experience.
• Debug and resolve production UI issues across browsers and devices.

Senior UI/Frontend Engineer – AI

3D Artist

• Produce high-impact product presentation videos for landing pages, demos, ads, and social content
• Design UI animations, micro-interactions, and product walkthroughs
• Create product mockups for web apps, dashboards, and mobile interfaces
• Develop animated branding assets including logos and dynamic visual identities
• Leverage AI creative tools to generate innovative visuals and concepts
• Collaborate closely with Product and Engineering teams to integrate design with functionality
• Contribute to product storytelling, user experience, and visual strategy

UI/UX Designer, Motion Designer

• Design and build scalable, production-ready web applications using React and Next.js
• Develop robust backend services and APIs using Node.js
• Architect end-to-end features across frontend, backend, and database layers
• Build real-time dashboards for trading performance, agent monitoring, and analytics
• Implement secure authentication, authorization, and role-based access systems
• Optimize frontend performance, UX, and state management
• Collaborate closely with AI, backend, and infrastructure engineers to integrate trading agents and execution systems
• Ensure code quality through testing, CI/CD, and best engineering practices
• Contribute to architectural decisions and long-term product scalability
• Help shape product direction in a fast-moving, high-ownership environment

Lead Fullstack Engineer

• Design, develop, and maintain robust backend applications using Golang
• Collaborate with product, frontend, and DevOps teams to ensure quality and performance
• Contribute to architectural decisions and the continuous improvement of our systems
• Ensure the security, scalability, and maintainability of solutions
• Mentor and share best practices with junior engineers