AgileEngine is an Inc. **** company that creates award-winning software for Fortune 500 brands and trailblazing startups across 17+ industries.
We rank among the leaders in areas like application development and AI/ML, and our people-first culture has earned us multiple Best Place to Work awards.WHY JOIN USAt AgileEngine we're looking for people who want to grow, make an impact, and work with people who care.
Let's create the future together!
ABOUT THE ROLESenior AI Engineer – a senior technical role focused on designing and building agentic workflows and AI-powered products that serve enterprise customers in higher education.MUST HAVES5+ years of experience in a software product development environment.Strong proficiency with Java and Spring Boot.Experience with relational databases.Hands‐on experience building AI agents, tool‐using LLM applications, or orchestrated workflows.Experience using agents to support coding, code review, testing, release management, or incident response.Senior‐level experience building and operating production software systems.Experience with TypeScript, Python, RESTful APIs and infrastructure on AWS.Experience with APIs, workflow engines, event‐driven architectures, integrations, or distributed systems.Strong understanding of testing, observability, security, permissions, and failure recovery.Ability to turn ambiguous opportunities into practical, reliable technical solutions.Strong communication skills and the ability to influence engineering practices across a team.Experience building enterprise SaaS or multi‐tenant systems.Leadership mindset and ability to drive changes across different teams.NICE TO HAVESExperience with multi‐agent systems, RAG, vector search, agent evaluation, or human‐in‐the‐loop workflows.Familiarity with higher education technology and its privacy and compliance requirements.Experience leading technical transformation or introducing new engineering practices.WHAT YOU WILL DODesign and build agentic workflows that coordinate tools, services, data, and human approvals.Develop reusable patterns for agent orchestration, tool use, context management, memory, and multi‐agent collaboration.Use agentic coding tools throughout the development lifecycle, including planning, implementation, testing, debugging, refactoring, code review, and documentation.Integrate agents into software development, testing, deployment, support, and operational workflows.Establish standards for agent evaluation, observability, reliability, security, and governance.Prototype new agentic capabilities and turn successful experiments into production systems.Build complete, usable products—not just demonstrations or isolated model integrations.Identify opportunities to improve engineering productivity and product delivery through agentic development.Mentor engineers and help shape an agentic‐first technical strategy across the organization.Collaborate with product and engineering leaders to connect agentic capabilities to meaningful outcomes.Demonstrate the ability to use AI coding agents as productive engineering collaborators across an application codebase.Provide agents with clear context, constraints, architecture,



acceptance criteria, and technical direction.Review, test, secure, and improve agent‐generated code rather than accepting outputs without verification.Decompose complex application work into agent‐ready tasks while maintaining architectural consistency and product intent.Create workflows in which agents can reason, select tools, call APIs, manage state, recover from failures, and escalte to people when appropriate.Design agentic applications around real user needs, including intuitive experiences, reliable workflows, permissions, audit trails, and meaningful error handling.Move from prototype to production by addressing performance, scalability, security, monitoring, evaluation, cost, and ongoing maintenance.Understand problems deeply, make appropriate tradeoffs, and deliver complete solutions that customers can depend on.PERKS AND BENEFITSProfessional growth – mentorship, TechTalks, and personalized growth roadmaps.Competitive compensation – USD‐based pay with education, fitness, and team activity budgets.Exciting projects – modern solutions with Fortune 500 and top product companies.Flextime – versátil schedule with remote and office options.About Our ClientOur client is a global data and AI consulting firm serving enterprise clients across multiple industries, including FMCG, financial services, healthcare, manufacturing, and the public sector, with a team of data and AI experts spread across 20+ countries.About the RoleWe're looking for a Senior Deployed AI Engineer specialized in Gemini Enterprise and the Google AI stack to design, build, and deliver full‐stack AI products for enterprise clients.
You'll work embedded with clients, take AI features from idea to production, and serve as the team's reference for Google's enterprise AI platform.You'll own your components end to end: the front end, the service behind it, the data and retrieval pipelines feeding it, the deployment, and the evaluations proving it works.
Beyond Google platform depth, you'll be expected to deliver confidently across the full stack, including full‐stack development, data engineering, cloud infrastructure, and client communication.ResponsibilitiesDevelop user‐facing interfaces in TypeScript and React, along with the backend services and APIs behind them in Python or Node.Implement agentic behavior including orchestration, tool and function calling, memory, and guardrails.Build retrieval‐augmented generation pipelines covering ingestion, chunking, embeddings, and vector and hybrid search.Design and build agents with Gemini models, Vertex AI, the Agent Development Kit, and Agent Engine.Implement and configure Gemini Enterprise for clients, including Agent Designer, the Inbox for managing long‐running agents,



and agent sandboxes.Connect Gemini Enterprise to client application landscapes through first‐party and partner connectors with proper permissions, governance, and auditability.Build grounded, retrieval‐backed applications with Vertex AI Search, RAG Engine, grounding with Google Search, and BigQuery as the data backbone.Implement agent interoperability through the A2A protocol and MCP, and track Google's releases closely to translate new capabilities into client value.Write evaluation suites and regression tests for LLM‐powered features, monitoring cost, latency, and quality in production.Deploy on cloud infrastructure across GCP, Azure, or AWS, and build and maintain the data pipelines feeding AI systems.Use agentic coding tools such as Claude Code, Gemini CLI, Codex, or Cursor daily with good judgment about verification and review.Communicate progress, trade‐offs, and blockers clearly to clients and project leads, and support pre‐sales when needed.Mentor junior engineers and contribute to internal accelerators, reusable components, and engineering standards.What We're Looking For3 to 5 years of software or data engineering experience, with extensive hands‐on use of AI tools and LLM‐based development over the past year.Strong hands‐on experience with the Google AI stack, including Gemini models, Vertex AI, and ideally Gemini Enterprise or ADK, with at least one solution taken to production on GCP.Experience with front‐end development in React or similar frameworks, and at least one back‐end framework.Hands‐on experience with RAG, embeddings, and vector search, and with at least one agentic framework such as Google ADK, LangGraph, or LangChain.Strong working experience with GCP; Azure or AWS is a plus.Fluency with agentic coding tools such as Claude Code, Gemini CLI, Codex, or Cursor, and experience building and maintaining data pipelines.Professional English proficiency at C1 or C2 level minimum, as you'll work daily with international clients and colleagues.Bachelor's or Master's degree in computer science, engineering, or a related field, or equivalent practical experience.A Google Cloud certification is a strong differentiator at application; if you don't hold one yet, obtaining one within the first two months is required, with exam sponsorship and prep time provided.
The preferred certification is Google Cloud Professional Machine Learning Engineer, covering Vertex AI, generative AI, and production ML.Nice to HaveExperience with MCP servers, multi‐agent patterns, or LLM evaluation tooling such as LangSmith, Langfuse, or promptfoo.Experience with Terraform or CI/CD pipelines.Experience with GCP, BigQuery, or Google Workspace integrations alongside Gemini Enterprise.Work Schedule100% remote setup so you can work wherever you're most productive.This position operates on a full‐time basis, with dedicated hours to ensure alignment with the team and delivery of quality work.Availability during US business hours.Compensation & Time OffCompensation paid in USD.Paid bi‐monthly on the 15th and 30th.Paid Time Off according to company policy.Holidays observed according to company guidelines.
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📌 Remote Agentic Ai Engineer For Scalable Cloud Systems (Buenos Aires)
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