Senior Fullstack AI Engineer (Remote)

LiveLike•New York, NY

78d•Remote

About The Position

We are seeking an exceptional Senior Full Stack AI Engineer to join our Genie team. You will be a key technical contributor responsible for designing, developing, and deploying end-to-end AI-powered applications that leverage Large Language Models (LLMs) and autonomous agent frameworks to revolutionize resource management. This role requires a unique blend of full-stack development expertise and deep AI/ML knowledge, with hands-on experience building production-grade GenAI and LLM-based solutions. You will work at the intersection of modern web development and cutting-edge AI technology, building scalable, intelligent systems that can reason, learn, and act autonomously to optimize resource allocation and management workflows.

Requirements

Programming Languages: 5+ years of professional experience with: Python (advanced proficiency) - Langraph, Langchain, NumPy, Pandas, scikit-learn
TypeScript/JavaScript (ES6+)
Strong understanding of object-oriented and functional programming paradigms
Full Stack Development: 4+ years of experience with: Frontend: Next.js, React, React Hooks, Context API, TailwindCSS, Shadcn/UI
Backend: Node.js, Fastify, FastAPI, Flask
APIs: RESTful design, GraphQL, WebSocket implementation
Databases: PostgreSQL, MongoDB, Redis
AWS: EC2, Lambda, S3, ECS/EKS, SageMaker, Bedrock, CloudWatch
CI/CD: GitHub Actions, Jenkins, GitLab CI
AI/ML Expertise: 2+ years hands-on experience with: LLM Integration: OpenAI API, Anthropic Claude, AWS Bedrock, Azure OpenAI
Agentic Frameworks: LangChain, LangGraph, CrewAI, AutoGen, Claude SDK or similar
Observability and Traceability: Arize Pheonix, Langfuse, LangSmith, AgentOps
RAG Systems: Building retrieval pipelines, chunking strategies, embedding generation
Vector Databases: Pinecone, Weaviate, FAISS, ChromaDB, or Qdrant
ML Libraries: TensorFlow, PyTorch, Hugging Face Transformers
Prompt Engineering: Advanced techniques for optimization and consistency
Bachelor's or Master's degree in Computer Science, Engineering, AI/ML, or related field
5-7 years of professional software engineering experience
2+ years of hands-on experience building production LLM/GenAI applications
Proven track record of deploying and scaling AI-powered applications in production environments
Experience with the complete software development lifecycle (SDLC)
Strong understanding of software architecture patterns and system design principles.

Nice To Haves

Experience with model fine-tuning and custom model deployment
Knowledge of machine learning algorithms, neural networks, and deep learning architectures
Familiarity with MLOps practices and tools (MLflow, Weights & Biases, Kubeflow)
Experience with streaming data pipelines (Kafka, Kinesis)
Understanding of AI safety, ethics, and responsible AI practices
Contributions to open-source AI/ML projects

Responsibilities

Design, develop, and deploy agentic AI systems using frameworks like LangChain, LangGraph, CrewAI, or AutoGen
Build and optimize multi-agent workflows with capabilities including: Semantic search based Retrieval Augmented Generation (RAG) for context-aware responses
Tool usage and API orchestration
Context Engineering with memory and user personalisation
Implement LLM-powered features including intelligent resource recommendations, automated workflow optimization, and predictive analytics
Develop and fine-tune prompts for various LLM models (GPT-4, Claude, Llama, etc.)
Design guardrails, human-in-the-loop patterns, and response verification systems to prevent hallucinations and ensure accuracy
Drive architecture for AI observability, safety and evaluation (precision, F1, hallucination detection, cost metrics).
Implement model evaluation frameworks and monitoring systems to track AI system performance
Build responsive, performant web applications using Next.js, React, and TypeScript
Design and implement RESTful APIs and GraphQL endpoints using Python (FastAPI/Flask) and Node.js
Develop backend services that integrate with LLM APIs (OpenAI, Anthropic, AWS Bedrock)
Create intuitive user interfaces that seamlessly incorporate AI capabilities
Implement real-time features using WebSockets for streaming LLM responses
Write clean, maintainable, well-documented code following best practices and design patterns
Deploy and manage applications on AWS services including: Lambda, ECS/EKS, S3, API Gateway, SageMaker, Bedrock
DynamoDB, RDS, ElastiCache
CloudWatch, X-Ray for monitoring and observability
Implement CI/CD pipelines for automated testing and deployment
Should have fair understanding of MLOPS
Design scalable, fault-tolerant architectures that can handle high-volume AI workloads
Optimize infrastructure costs while maintaining performance and reliability
Collaborate with product managers, designers, and stakeholders to translate business requirements into technical solutions
Participate in code reviews, architectural discussions, and technical design sessions
Mentor junior engineers and share best practices for AI-driven development
Stay current with the latest advancements in LLMs, agentic AI, and machine learning techniques
Document system architectures, API specifications, and implementation details
Contribute to the evolution of our AI development standards and practices