Python/AI Full Stack Developer

Elite

United States

2 days ago

Role details

Contract type

Permanent contract

Employment type

Full-time (> 32 hours)

Experience level

Expert

Experience required

2 years minimum

Working hours

Regular working hours

Job source

www.dice.com

Tech stack

JavaScript (Programming Language) Application Programming Interfaces (APIs) Artificial Intelligence Business Logic Automation of Tests BigQuery Cloud Engineering Continuous Integration Data Integration Information Leak Prevention Data Stores Data Systems

+28 more

Relational Databases Enterprise Content Management Python (Programming Language) PostgreSQL Machine Learning Software Architecture Cloud Services Search Technologies Software Deployment Software Engineering TypeScript Web Applications Web Services Workflow Management Systems Dynamic Routing Google Cloud ReactJS Large Language Models Multi-Agent Systems Prompt Engineering Generative AI Indexer Backend Containerization AI Platforms Information Technology Data Management Docker

Job description

The Senior Full-Stack Engineer is a hands-on, deeply technical position responsible for designing, building, and deploying cutting-edge Generative AI software and multi-agent systems.
This role owns the end-to-end implementation of the foundation’s intelligent applications, from engineering advanced backend services on Google Cloud Vertex AI utilizing Gemini Enterprise models, to orchestrating complex workflows with Google’s Agent Development Kit (ADK) and connecting enterprise content and data through the Model Context Protocol (MCP).
The engineer will also build interactive web user experiences in React and manage structural and semantic vector data in PostgreSQL. Qualifications
Design, develop, and deploy enterprise-scale multi-agent systems using Google’s Agent Development Kit (ADK), including multi-step autonomous workflows, API and tool calling, stateful conversation management, and human-in-the-loop patterns.
Build and maintain Model Context Protocol (MCP) clients and servers to provide standardized connectivity between LLM applications, enterprise content repositories, data platforms, APIs, and web services.
Design scalable full-stack architectures connecting AI services, application logic, relational and vector data stores, and interactive web applications.
Integrate Gemini models through Google Cloud Vertex AI, configuring context management, prompt templates, structured outputs, model behavior, and appropriate safety controls.
Develop clean, maintainable, production-grade Python services supporting application logic, AI orchestration, LangChain workflows, and data integration pipelines.
Build modern, responsive web applications using React and JavaScript/TypeScript, including interfaces capable of streaming and displaying agent states and model responses.
Design and optimize data solutions using PostgreSQL, pgvector, and BigQuery, supporting relational, keyword, semantic vector, and hybrid search use cases.
Develop and optimize Retrieval-Augmented Generation (RAG) pipelines that provide LLMs and agents with accurate, relevant enterprise context.
Containerize and deploy full-stack AI applications within Google Cloud Platform, incorporating automated testing and modern CI/CD practices.
Monitor and optimize token consumption, model selection and routing, semantic caching, application latency, model accuracy, and cloud costs.
Implement evaluation and observability capabilities for AI applications, including agent tracing, response quality monitoring, hallucination detection, model drift identification, and dynamic routing evaluation.
Implement AI security controls to mitigate risks including prompt injection, sensitive-data leakage, and unauthorized PII exposure.
Apply responsible AI engineering practices, including automated validation and evaluation of prompts and model responses for quality, transparency, fairness, and reliability.
Work within an Agile delivery environment and independently take solutions from initial design through development, testing, deployment, and production support.
Collaborate with architects, engineers, product teams, and other stakeholders to translate business requirements into scalable technical solutions.

Requirements

6+ years of professional software engineering experience, with a background in full-stack development, software architecture, machine learning engineering, or a related discipline.
2+ years of hands-on Generative AI experience, building and deploying enterprise LLM applications, RAG solutions, or agentic/multi-agent systems into production environments.
Advanced programming skills in Python, including experience developing production-grade backend applications and services.
Strong hands-on experience with Google Cloud Platform (Google Cloud Platform) and Vertex AI.
Production experience working with Gemini or comparable enterprise LLM platforms.
Practical experience developing agentic AI solutions using Google Agent Development Kit (ADK) or comparable agent orchestration frameworks.
Experience implementing or working with Model Context Protocol (MCP) clients and/or servers.
Strong understanding of RAG architectures, embeddings, vector search, context management, prompt engineering, and LLM orchestration.
Experience with LangChain or similar AI application frameworks.
Proficiency with React and modern JavaScript/TypeScript for developing single-page web applications and integrating streaming APIs.
Strong knowledge of PostgreSQL, including relational data modeling, advanced querying, and vector indexing/search using pgvector or similar technologies.
Experience with BigQuery or comparable cloud data platforms.
Working knowledge of Docker, automated testing, CI/CD pipelines, and cloud-native application deployment.
Understanding of Generative AI observability, evaluation, security, model performance, token optimization, and cost management.
Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Software Engineering, or a related discipline, or equivalent practical experience. Preferred Skills
Experience designing enterprise-scale autonomous or multi-agent AI systems.
Strong understanding of agent memory, tool calling, workflow orchestration, and human-in-the-loop architectures.
Experience implementing hybrid search combining traditional keyword retrieval and semantic vector search.
Familiarity with LLM evaluation frameworks, tracing, hallucination detection, and model quality monitoring.
Knowledge of responsible AI practices and security considerations specific to enterprise Generative AI applications.
Experience taking AI applications from proof of concept through scalable production deployment.