Job Url: https://www.linkedin.com/jobs/search/?currentJobId=4341571741&distance=25&f_AL=true&f_TPR=r86400&f_WT=2&geoId=103644278&keywords=software%20engineer&origin=JOB_SEARCH_PAGE_JOB_FILTER&refresh=true&start=175 Job Description: Full Stack Engineer – AI & Distributed Systems New York City Metropolitan Area · 8 hours ago · Over 100 applicants Promoted by hirer · No response insights available yet $180K/yr - $350K/yr Remote Matches your job preferences, workplace type is Remote. Full-time Easy Apply Save Save Full Stack Engineer – AI & Distributed Systems at Undisclosed Full Stack Engineer – AI & Distributed Systems Undisclosed · New York City Metropolitan Area (Remote) Easy Apply Save Save Full Stack Engineer – AI & Distributed Systems at Undisclosed Show more options Get personalized tips to stand out to hirers Practice mock interviews personalized to every role and get custom feedback Try Premium for PKR0 People you can reach out to University of the Punjab logo School alumni from University of the Punjab Show all Meet the hiring team Marijo Amoyo 3rd COO @ Mobiusengine.AI Job poster Message About the job Overview Our client is seeking a highly skilled Full Stack Engineer with deep AI engineering experience to design, build, and scale next-generation intelligent applications used globally by enterprises and end-users. This role is ideal for an engineer who combines backend expertise, frontend excellence, and hands-on AI/ML engineering capabilities—comfortable building everything from distributed microservices to inference pipelines to highly polished UI surfaces. You will work across the entire stack: AI model integration & LLM orchestration Vector search & embedding pipelines Scalable microservices Data processing and feature engineering Frontend web app architecture Cloud-native infrastructure (Kubernetes, serverless, GPU-backed systems) This role will partner closely with product, design, data science, and platform engineering to deliver intelligent, high-performance systems that power the company’s AI-driven suite. Key Responsibilities Full Stack Architecture & System Design Design and build end-to-end application architectures spanning backend microservices, frontend UI layers, and machine learning inference paths. Architect data workflows for: LLM prompting, chaining, and agent execution Embedding generation and vector retrieval Streaming and event-driven services (Kafka, Pub/Sub) Implement scalable APIs and backend services using Node.js, Python (FastAPI / Flask), Go, or Java. Own technical design documents, architectural reviews, RFCs, and cross-team engineering alignment. Backend Engineering & Distributed Systems Build high-throughput distributed services with microservice patterns (gRPC, REST, event-driven). Implement AI workflow orchestration and model-serving endpoints for LLMs, fine-tuned models, and multi-model routing. Use distributed caching, queueing, and pub-sub systems for low-latency AI applications. Optimize performance across compute, memory, concurrency, and horizontal scalability. Implement robust testing frameworks across unit, integration, load, and performance layers. Tech examples may include: Node.js, Python, Go, Redis, Kafka, Postgres, MongoDB, Elasticsearch, gRPC, Docker, Kubernetes, Terraform. AI Engineering & Machine Learning Systems Build AI-powered features using: LLMs (OpenAI, Anthropic, Mistral, Llama) Embedding models (text-embedding, multi-modal) Vector databases (Pinecone, Weaviate, FAISS, pgvector) Model-serving frameworks (TensorRT, ONNX Runtime, Triton Inference Server) Develop pipelines for: Document chunking Embedding generation Retrieval-augmented generation (RAG) Prompt optimization and evaluation Use AI tools/frameworks such as LangChain, LlamaIndex, HuggingFace Transformers. Frontend Engineering & User Experience Build intuitive, high-performance web applications using: React, Next.js, TypeScript Tailwind, MUI, or custom design systems GraphQL/REST/GRPC clients WebSockets, SSE for real-time interactions Implement AI-native UX patterns (chat interfaces, agent dashboards, AI copilots, model results visualization). Collaborate with design and product to deliver refined, responsive experiences across web and mobile browsers. Cloud Infrastructure, DevOps & Observability Deploy workloads on AWS, GCP, or Azure, including GPU-backed environments for inference. Build CI/CD pipelines (GitHub Actions, ArgoCD, GitLab CI) to safely ship code multiple times per day. Use infrastructure-as-code (Terraform, Helm) to manage cloud resources. Instrument monitoring and observability (Prometheus, Grafana, Datadog, OpenTelemetry). Optimize cloud costs across compute, storage, embeddings, and AI inference. Security, Compliance & AI Governance Apply secure coding best practices across backend and frontend systems. Implement guardrails and governance for AI systems: prompt injection mitigation model hallucination detection safe output filtering user data privacy & PII redaction Collaborate with security teams on: IAM principles Role-based access control API authentication & authorization Data encryption (in transit & at rest) Cross-Functional Collaboration Work closely with: Product to refine AI capabilities and refine user workflows Data Science & ML on model evaluation, tuning, and feature ideation Design on AI-first UX patterns Platform Engineering on scalable pipeline architecture Participate in sprint planning, architecture reviews, incident response, and release planning. Qualifications 7–12+ years of professional engineering experience across backend + full stack development. Strong proficiency in JavaScript/TypeScript, Python, or Go. Hands-on experience with LLMs, embeddings, vector databases, and AI/ML pipelines. Strong knowledge of modern web development: React/Next.js, TypeScript, state management patterns. Experience with distributed systems, microservices, event-driven architectures. Proficiency with relational and NoSQL data stores (PostgreSQL, Redis, MongoDB, Elasticsearch). Experience deploying and scaling systems in AWS/GCP/Azure environments. Strong grasp of DevOps, container orchestration (Kubernetes), and CI/CD pipelines. Experience working in scaling environments (500–2,000+ employee tech orgs preferred). Bachelor’s degree in Computer Science or related field (Master’s preferred). Leadership Attributes Deep technical curiosity: passionate about AI, distributed systems, and modern full stack architectures. End-to-end owner: comfortable owning entire features from backend logic to frontend UI. High craftsmanship: cares deeply about performance, structure, testing, and reliability. Innovative builder: brings creativity to solving complex engineering and AI challenges. Collaborative partner: communicates clearly, works cross-functionally, and elevates team engineering maturity. Strategic problem-solver: aligns engineering decisions with product goals and long-term system health. Why This Role This is a chance to build AI-native applications inside a fast-scaling SaaS/AI company—shaping the foundation of intelligent products reaching millions of users. You’ll own high-impact features, influence architectural strategy, and build sophisticated systems at the frontier of modern engineering: LLM integration, multi-agent systems, real-time inference, distributed pipelines, and full stack product engineering. If you're a full stack engineer who thrives on technical depth, AI innovation, and end-to-end product creation, this role is a career-defining opportunity.