Agentic Engineer [Zeal]

Zeal is an AI-enabled software consultancy purpose-built for Fortune 1000 companies, and we’re now part of Livefront . We help companies navigate technology to drive business outcomes by attracting, retaining, and growing exceptional talent and empowering them to use innovative technologies and process frameworks.

We’re a values-driven technology company grounded in kindness, generosity, and integrity. Our mission is to build a place where our employees love their future and where clients trust us to deliver what we promise: to improve their business through technology. Love your future.

We’re looking for an outstanding Agentic Engineer to join our team. This role is available across our primary LATAM hub in Peru.

Who you are

You are a pragmatic, hands-on engineer who has built and shipped agentic AI systems into production. You understand that getting an agent to work is only part of the challenge. You know how to build systems that continue to work safely and reliably with real data, enterprise security requirements, and meaningful consequences when something goes wrong. You communicate clearly with clients and technical peers, make thoughtful architectural decisions, and are comfortable explaining why an agent behaved the way it did. You have strong software engineering fundamentals and the judgment to recognize when an agent is the right solution and when a simpler, deterministic approach is better.

What you will be doing

Work directly with clients to understand complex business problems and translate them into scalable, production-ready agentic AI solutions.

Design and implement multi-step agent systems, including orchestration, tool and function calling, task decomposition, and, when appropriate, multi-agent coordination.

Build integrations between agents and enterprise systems by exposing APIs as tools, implementing MCP servers, and developing structured tool contracts.

Design evaluation frameworks, guardrails, grounding and citation checks, and human-in-the-loop workflows that identify and manage agent failures before they reach users.

Instrument agent systems for traceability and observability, including agent actions, model behavior, token and cost usage, latency, and system performance.

Design and maintain state and memory across long-running workflows and implement model routing, retries, idempotency, and other patterns required for reliable production systems.

Build and deploy AI solutions using cloud platforms and services such as:

Azure OpenAI, Azure AI Foundry, Azure AI Search, AKS, or related Azure technologies;

Amazon Bedrock, Lambda, ECS, EKS, or related AWS technologies.

Apply strong software engineering practices using Python, Java, APIs, containers, source control, automated testing, and CI/CD.

Work directly with client engineering and security teams through architecture reviews, threat modeling, security reviews, audits, and other enterprise delivery requirements.

Evaluate when agentic architectures are appropriate and when deterministic pipelines, traditional machine learning, or other approaches are more reliable and cost-effective.

Turn successful approaches from client engagements into reusable patterns and solutions that can be applied across Zeal.

Build trust with clients and internal teams through strong communication, technical judgment, problem-solving, and execution.

Why you should apply

You want to work with passionate, talented people who are always looking for ways to make things better.

You value a work environment where kindness, mutual trust, and egoless collaboration are paramount.

You have a history of keeping promises, doing what is right, and spending energy on what matters.

You enjoy the variety and growth that comes with consulting for Fortune 1000 companies across industries and technical stacks.

You want to work on projects that have outsized impact and reach.

You believe in sweating the details, committing to quality, and taking pride in going the extra mile.

What you bring to the table

We expect candidates to demonstrate a strong software engineering foundation, hands-on experience building agentic AI systems in production, and a keen interest in consulting.

Agentic AI: Hands-on experience designing and shipping production agent systems involving tool and function calling, multi-step workflows, orchestration, task decomposition, or multi-agent architectures. You can explain the architectural decisions you made, what failed, and what you learned.

Evaluation & Reliability: Real-world experience evaluating agent behavior and measuring system failures, including building evaluation harnesses, guardrails, grounding checks, human-in-the-loop controls, and other mechanisms that prevent or mitigate errors.

Cloud Technologies: Deep hands-on experience with either Azure or AWS and the AI and infrastructure services surrounding them, such as Azure OpenAI, AI Foundry, AI Search, AKS, Amazon Bedrock, Lambda, ECS, or EKS.

Software Engineering: Strong proficiency in Python or Java, API design, containers, automated testing, source control, and CI/CD, with enough machine learning knowledge to determine when traditional ML or deterministic approaches are more appropriate than an LLM.

Enterprise Delivery: Experience delivering software within large organizations and working through requirements such as role-based access, data isolation, security reviews, compliance, change control, and production governance.

Agent Observability: Experience designing systems for traceability, state and memory management, model routing, latency and cost visibility, retries, idempotency, and other operational concerns associated with production agent systems.

Communication: Excellent verbal and written communication skills and the ability to explain complex AI systems, architectural decisions, risks, and tradeoffs to both technical and non-technical stakeholders.

Problem-Solving: Strong technical judgment and a demonstrated willingness to challenge assumptions, including recognizing when an agentic solution introduces unnecessary complexity and a deterministic approach would be more appropriate.

Experience with MCP and emerging tool-interoperability standards, evaluation frameworks such as RAGAS, fine-tuning, model distillation or optimization, and highly regulated industries such as financial services, insurance, healthcare, or manufacturing is helpful, but not required.

What to expect

When applying, please include a short note about yourself, a summary of your work experience, and a link to any public profiles you actively maintain (e.g., GitHub, LinkedIn). Our hiring process moves quickly and consists of several stages for candidates who capture our attention with their initial submission, sometimes including but not limited to a short preliminary phone interview, a series of video interviews, and a short take-home exercise, which you'll have up to a week to complete.

Additional information

We go out of our way to evaluate all employees and job applicants equitably based on merit, competence, and qualifications. We encourage candidates from all backgrounds and identities to apply, and we consider every applicant with an open mind. Don't worry, every application will be reviewed by a human.