> Markdown version of [/jobs/ext/1283252-data-engineer](https://www.wearedevelopers.com/jobs/ext/1283252-data-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Data Engineer - **Company:** Publicis Groupe - **Location:** New York, NY, United States - **Salary:** $110,000.0 - **Contract:** Permanent contract - **Skills:** JavaScript (Programming Language), A/B Testing, Artificial Intelligence, Amazon Web Services, Amazon Elastic Compute Cloud, Amazon S3, Application Release Automation, Automated Storage and Retrieval Systems, Microsoft Azure, Cloud Computing, Code Review, Databases, Continuous Integration, Distributed Systems, Python (Programming Language), PostgreSQL, Software Architecture, Redis, Software Deployment, Software Engineering, Management of Software Versions, Web Application Frameworks, ReactJS, Large Language Models, Multi-Agent Systems, Prompt Engineering, Model Validation, Generative AI, Backend, Containerization, AngularJS, Kubernetes, Infrastructure Automation Frameworks, Graphql, Machine Learning Operations, Front End Software Development, Functional Programming, Api Design, Restful APIs, Docker, Amazon Redshift - **Published:** July 15, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=622d3a3e3c903762 ## About the Role * Strong software engineering fundamentals in Python and JavaScript, with experience in production-grade application development * Proficiency in modern JavaScript frameworks such as React and Angular for building AI-powered user interfaces and tooling * Deep hands-on experience with LLMs, prompt engineering, and generative AI frameworks (LangChain, LlamaIndex, or similar) * Experience with advanced AI patterns beyond basic RAG, including autonomous agents, multi-step reasoning, tool use, and multi-model orchestration * Proficiency in MLOps tooling and practices: model serving (e.g., vLLM, TGI), experiment tracking, and CI/CD for ML * Strong experience with Azure DevOps for pipeline management, release automation, and infrastructure-as-code * Solid knowledge of cloud infrastructure, networking, and security on AWS and Azure * Hands-on experience with AWS services including S3, Lambda, EC2, and related compute and storage offerings * Working knowledge of relational and specialized databases, including PostgreSQL, Amazon Redshift, Qdrant (vector database), and Redis * Experience with containerized deployments (Docker, Kubernetes) and infrastructure automation * Solid understanding of software architecture patterns, API design (REST/GraphQL), and distributed systems * Experience with model evaluation, A/B testing, and performance benchmarking for AI systems, * You build AI systems that are production-ready from day one: reliable, observable, and maintainable * You think beyond off-the-shelf solutions and are excited to explore what is newly possible with AI * You think in terms of architecture and scalability, not just proof-of-concept demos * You proactively identify failure modes, edge cases, and safety considerations before they reach production * You can operate independently while collaborating effectively across engineering, product, and data teams * You communicate complex technical concepts clearly to both technical and non-technical stakeholders ## Description We are looking for a AI Engineer to lead the design, development, and deployment of AI-powered systems across the organization. This role sits at the intersection of full-stack software engineering and applied AI, with a strong emphasis on large language models (LLMs), generative AI, and production-grade ML infrastructure. You will push beyond conventional approaches to explore the advanced possibilities of AI, including autonomous agents, multi-model orchestration, and novel architectures. This is a hands-on engineering role for someone who can build reliable, scalable AI systems from prototyping and prompt engineering through to production deployment, monitoring, and iteration. * Design, build, and maintain production AI/ML systems, including LLM-based applications, agentic workflows, multi-model pipelines, and advanced retrieval systems * Explore and prototype cutting-edge AI capabilities beyond standard patterns, such as autonomous agents, tool-use frameworks, reasoning chains, and self-improving systems * Develop and optimize prompt engineering strategies, fine-tuning approaches, and evaluation frameworks for generative AI models * Architect and implement MLOps pipelines for model training, versioning, deployment, and monitoring using Azure DevOps and cloud infrastructure * Build robust APIs, backend services, and frontend interfaces to integrate AI capabilities into internal and customer-facing products * Evaluate and integrate foundation models (OpenAI, Anthropic, Google) based on performance, cost, and latency requirements * Implement guardrails, safety mechanisms, and observability tooling for deployed AI systems * Collaborate with product, data, and engineering teams to identify high-impact AI use cases and translate them into technical solutions * Follow engineering best practices, including code review, testing, CI/CD, and documentation for AI codebases * Stay current with the rapidly evolving AI landscape and drive adoption of emerging tools, frameworks, and techniques, * AI systems are deployed to production with clear SLAs, monitoring, and rollback capabilities * LLM-powered features deliver measurable business value with consistent quality and reliability * The team is actively exploring and shipping advanced AI capabilities that go beyond industry-standard approaches * MLOps infrastructure enables rapid experimentation and safe, repeatable model deployments * AI is treated as a core engineering discipline with proper testing, documentation, and operational rigor ## Related Videos - [Watch Tests Go Brrrr! : Getting Started with Cypress in ReactJS](https://www.wearedevelopers.com/videos/282-watch-tests-go-brrrr-getting-started-with-cypress-in-reactjs) - [Docker Compose: Rediscovered](https://www.wearedevelopers.com/videos/1978-docker-compose-rediscovered) - [Reducing LLM Calls with Vector Search Patterns - Raphael De Lio (Redis)](https://www.wearedevelopers.com/videos/1714-reducing-llm-calls-with-vector-search-patterns-raphael-de-lio-redis) - [Postgres in the Age of AI (and Devin)](https://www.wearedevelopers.com/videos/1042-postgres-in-the-age-of-ai-and-devin) - [Docker build without Docker](https://www.wearedevelopers.com/videos/100114-docker-build-without-docker) - [Coffee with Developers - Maria Apazoglou](https://www.wearedevelopers.com/videos/1209-coffee-with-developers-maria-apazoglou) ## Related Articles - [Navigating the AI Shift](https://www.wearedevelopers.com/magazine/629-navigating-the-ai-shift) - [Dev Digest 120 - Apple and peers](https://www.wearedevelopers.com/magazine/455-dev-digest-120-apple-and-peers) - [Dev Digest 121 - AI goes offline](https://www.wearedevelopers.com/magazine/456-dev-digest-121-ai-goes-offline) - [Graph and AI Trends 2026: Why Is AI Running but Not Yet Delivering?](https://www.wearedevelopers.com/magazine/680-graph-and-ai-trends-2026-why-is-ai-running-but-not-yet-delivering) - [Dev Digest 137 - AI'm not sure about this](https://www.wearedevelopers.com/magazine/485-dev-digest-137-ai-m-not-sure-about-this) - [Dev Digest 132 - Binging WADFlix?](https://www.wearedevelopers.com/magazine/473-dev-digest-132-binging-wadflix)