Full Stack Engineer Enterprise AI Applications

Overview

Were seeking an exceptional Full Stack Engineer to build and scale our enterprise AI applications. Youll design and implement complete AI-powered features from database to UI working with cutting-edge LLM technology RAG systems and production ML infrastructure. This role combines full-stack development expertise with hands-on AI/ML engineering deploying intelligent systems that deliver real business value at scale.

Youll be a key technical contributor shipping production-ready AI features that users love while ensuring reliability performance and cost-effectiveness. This is an opportunity to work at the intersection of software engineering and artificial intelligence solving complex problems with modern technology.

What Youll Build

AI-Powered Applications

• Design and implement end-to-end RAG (Retrieval-Augmented Generation) pipelines that enable intelligent document search and question-answering across enterprise knowledge bases
• Build production-ready integrations with leading LLMs (GPT-4 Claude Gemini) that provide accurate contextual responses to user queries
• Develop sophisticated prompt engineering strategies and evaluation frameworks to ensure consistent high-quality AI outputs
• Create agent systems with tool integration capabilities that can autonomously complete complex tasks
• Implement vector search solutions using Pinecone Weaviate or similar technologies for semantic similarity and knowledge retrieval

Full-Stack Features

• Build scalable backend services using Python/FastAPI with type-safe APIs authentication and robust error handling
• Develop responsive performant frontend applications using React/ with real-time streaming for LLM responses
• Design and optimize database schemas spanning PostgreSQL MongoDB and Redis to support high-throughput AI workloads
• Implement WebSocket servers and event-driven architectures for real-time user experiences
• Create comprehensive testing strategies covering unit integration and end-to-end tests

Production Infrastructure

• Deploy and manage ML/AI services using Docker containers and Kubernetes orchestration
• Build and maintain CI/CD pipelines that enable rapid safe deployment of AI features
• Implement infrastructure as code using Terraform to manage cloud resources (AWS Azure or GCP)
• Set up comprehensive monitoring and observability using Datadog Prometheus/Grafana and LLM-specific tools (LangSmith Weights & Biases)
• Optimize costs through intelligent caching batching strategies and model selection algorithms
• Ensure enterprise-grade security with proper authentication authorization secrets management and compliance measures

Required Experience & Skills

Full-Stack Development (4 years)

•  Expert-level proficiency in Python with modern frameworks (FastAPI Flask)
• Strong TypeScript/JavaScript skills with deep React and experience
• Proven track record designing and building RESTful and GraphQL APIs
• Solid understanding of relational (PostgreSQL MySQL) and NoSQL (MongoDB) databases
• Experience with authentication systems (OAuth2 JWT SSO) and security best practices
• Track record of shipping high-quality scalable software to production

AI/ML Engineering (3 years)

• Hands-on experience building and deploying AI/ML applications in production environments
• Deep understanding of LLM integration prompt engineering and context management
• Proven expertise with RAG systems: document processing chunking embedding retrieval and generation
• Experience working with vector databases (Pinecone Weaviate Chroma FAISS or Qdrant)
• Strong grasp of semantic search similarity algorithms and hybrid search techniques
• Knowledge of evaluation frameworks for assessing AI system quality and performance

MLOps & Infrastructure (3 years)

• Production experience with Docker containerization and Kubernetes orchestration
• Strong knowledge of at least one major cloud platform (AWS Azure or GCP) and their AI services
• Experience building CI/CD pipelines for ML/AI applications
• Proficiency with infrastructure as code tools (Terraform CloudFormation Pulumi)
• Understanding of monitoring logging and alerting best practices
• Cost optimization experience for cloud and AI workloads

Software Engineering Excellence

• Strong computer science fundamentals and algorithmic thinking
• Experience with test-driven development (TDD) and comprehensive testing strategies
• Proficiency with Git workflows code review practices and collaborative development
• Excellent debugging and problem-solving skills
• Clear technical communication and documentation abilities
• Agile/Scrum experience with ability to work in fast-paced environments

Qualifications :

Preferred Qualifications
Advanced AI Capabilities

• Experience with LangChain LlamaIndex LangGraph or similar LLM frameworks
• Knowledge of fine-tuning techniques (LoRA QLoRA) and parameter-efficient methods
• Familiarity with agent architectures tool-using systems and Model Context Protocol (MCP)
• Experience with multi-modal AI (vision-language models document understanding)
• Background in prompt optimization structured outputs and function calling

Extended Technical Skills

• Additional programming languages: Go Rust or backend experience
• Advanced Kubernetes knowledge: Helm operators service mesh (Istio)
• Experience with message queues (Kafka RabbitMQ AWS SQS) and event-driven architectures
• Knowledge of graph databases (Neo4j) for advanced memory systems
• Contributions to open-source AI/ML projects

Leadership & Collaboration

• Experience mentoring junior engineers and conducting technical interviews
• Track record of making impactful architectural decisions
• Ability to translate complex technical concepts for non-technical stakeholders
• Experience working across teams (product design data science)

Additional Information :

All your information will be kept confidential according to EEO guidelines.

Remote Work :

No

Employment Type :

Full-time