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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior Data Scientist - AI Integration - **Company:** LEYTON INC - **Location:** Layton, FL, United States (Remote available) - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** LangGraph Framework, Clean Code Principles, Java (Programming Language), Multitier Architecture, Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Application Integration Architecture, Business Logic, Microsoft Azure, Cloud Computing, Code Review, Encodings, Databases, Continuous Integration, Data Structures, Software Design Patterns, DevOps, Django Web Framework, Elasticsearch, Github, Graph Database, Information Retrieval, Python (Programming Language), Search Algorithms, PostgreSQL, Machine Learning, MongoDB, Node.Js, Open Source Technology, Performance Tuning, RabbitMQ, Redis, OpenAI, Software Systems, Data Streaming, TypeScript, Pinecone, Data Processing, Qdrant, ReactJS, Retrieval-Augmented Generation, Delivery Pipeline, Large Language Models, Prompt Engineering, Llamaindex, Git, Fastapi, Event Driven Architecture, Gitlab-ci, Solid Principles, Kubernetes, Information Technology, Apache Kafka, Non-relational Database, Search Engines, Milvus, Graphql, Machine Learning Operations, FAISS, Api Design, Restful APIs, Terraform, Docker, Microservices - **Published:** November 10, 2026 - **Apply:** https://arc.dev/remote-jobs/j/redirect/py9tc382nn ## About the Role * System Design & Architecture: Ability to design distributed, scalable, and resilient systems. Strong command of architectural trade-offs. * Data Structures & Algorithms: Solid algorithmic expertise for performance optimization (Big-O, streaming and batch data processing). * Clean Code & Patterns: Strict application of SOLID principles and mastery of design patterns (including microservices and event-driven architectures). * CI/CD & DevOps: Advanced Docker, orchestration (Kubernetes is a plus), GitHub Actions / GitLab CI, Terraform / Infrastructure as Code. * APIs & Databases: Design of robust APIs (REST, gRPC, GraphQL). Optimization of relational (PostgreSQL) and non-relational databases (MongoDB, Redis, graph databases). AI & Data - Technical Expertise * LLM Integration & Ops (LLMOps): Advanced API integration, complex prompt engineering, token/cost management, streaming, continuous monitoring, and evaluation of models in production. * RAG & Agent Ecosystem: Proficiency with LangChain, LlamaIndex, or custom solutions. LangGraph. Large-scale implementation of Vector Stores (FAISS, Pinecone, Qdrant, Milvus). Advanced chunking and reranking strategies. * Information Retrieval: Solid understanding of search algorithms (TF-IDF, BM25, Cross-Encoders) and search engines (Elasticsearch). Tech Stack - Tech Agnostic, With Strong Appetite For Python * TypeScript / Node.js * Java / Spring * FastAPI / Django * React * PostgreSQL * Redis * Vector DBs AWS / GCP / Azure * Docker / Kubernetes * Git / CI/CD * LangChain / LlamaIndex * OpenAI API * Elasticsearch * Kafka / RabbitMQ, * Engineering degree or Master's (Computer Science, Mathematics, Data Science) with 3 to 5 years of relevant experience. * Proven track record: You have already designed, developed, and deployed software solutions integrating AI (Machine Learning, NLP, or GenAI) with measurable business impact. * Problem-solving & Autonomy: Ability to take a vague business need and turn it into a solid technical architecture and finished product. * Communication & Leadership: Strong interpersonal skills to engage with demanding clients and the ability to rally a technical team. * Technological Watch: Insatiable curiosity for a fast-moving AI ecosystem. ## Description As an experienced AI Software Engineer, you will play a driving role within our multidisciplinary team (Data Scientists, DevOps, Consultants). You will be responsible for architectural design, development, and large-scale deployment of AI-based technology solutions for both internal and external clients. You will tackle complex, open-ended problems requiring sharp technical decisions, uncompromising engineering rigor, and a strong capacity for innovation. Responsibilities Architecture & Development: Design, architect, and develop complete, scalable features and platforms (APIs, business logic, databases, security). AI Engineering: Lead the implementation of complex AI systems in production: advanced RAG pipelines, LLM agent fleets, model fine-tuning and integration (OpenAI, Anthropic, open-source LLMs). Quality & Clean Code: Champion best practices (SOLID principles, Clean Architecture, design patterns) and conduct thorough code reviews. Reliability & Testing: Define the testing strategy (TDD, unit, integration, e2e, stress tests) to ensure flawless deployments. MLOps & Cloud: Deploy, monitor, and optimize models and code in dedicated environments with robust CI/CD pipelines. Client Facing & Advisory: Act as a technical expert with clients. Understand their business challenges, break down complex concepts, and defend your architectural decisions. Mentoring: Support the growth and skill development of junior developers and DataLab interns.