Full Stack Engineer – AI & Distributed Systems

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. Apply tot his job Apply To this Job