6 publicaciones de empleo destacadas de engineer python developer en ciudad de méxico, dif (¡Están contratando!)

Software Engineer, AI Agent Platform
QAD —Ciudad de México, CDMX
- Tiempo completo
- Experiencia en manufactura
- Liderazgo
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Consultor Snow Flake Senior
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- DevOps
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Infosys —Ciudad de México, CDMX
- Oracle
- DevOps
- Inglés
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Consultor Snow Flake Senior
NetPartners International —Ciudad de México, CDMX
Experiencia general: mínimo 5 años en proyectos de Data Engineering / Data Warehousing. El profesional será responsable de desarrollar soluciones Data Warehouse…
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Software Engineer, AI Agent Platform

QAD -
Ciudad de México, CDMX

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Información del empleo

Tiempo completo
Hace 1 día

Cualificaciones

Derecho
Kubernetes
XML
Git
Docker
PostgreSQL
Experiencia en manufactura
Liderazgo
Python
Gestión de identidades y accesos

Descripción completa del empleo

Company Description

Redzone is the #1 Connected Workforce Solution for manufacturers big and small. We work to improve efficiency in plants, provide coaching for best practices, and enable the front-line worker to improve the quality of their work and their work life by providing them with tools, processes, and collaboration tools to keep their manufacturing lines running smoothly and efficiently.

At Redzone we focus on the customer experience, listening to the customer, and providing solutions that create great outcomes. We are a combination of great leadership, years of manufacturing experience, and an incredible technology team that all work together to create great products.

This role is fully remote.

Job Description

ChampionAI is QAD | Redzone agentic platform, purpose-built for manufacturing and utilized by the various business units within QAD | Redzone. This engineer joins the core platform team with three main areas of focus: building new capabilities into the platform, helping Applied AI teams at partner business units build and ship Champions correctly, and directly building Champion agents for business-unit use cases when needed.
You will work in the same codebase as the Applied AI engineers you support, which keeps the enablement work
practical and the platform work focused on real problems.

Key Responsibilities
Platform Development

Build and ship features across the Champion platform repositories
Improve developer experience: tooling, scaffolding, internal documentation, and onboarding paths for Applied AI engineers.
Maintain and evolve the MCP tool server and agent infrastructure that BU teams depend on.
Identify and address friction points that slow down Champion development or deployment

Applied AI Enablement

Support BU Applied AI engineers in building, deploying, and operating Champions correctly
Review agent implementations for prompt quality, scope enforcement, auth configuration, and deployment setup
Contribute to internal engineering guides and skill documentation
Pair with BU engineers on first K8S manifest creation, database registration, and LaunchDarkly prompt rollout

Agent Development

Design and build Champion agents for BU use-cases when the platform team is directly engaged
Write and iterate on system prompts, tool bindings, and context injection for production agents
Register agents in Champion Server and configure LaunchDarkly-gated prompt rollout across environments

Engineering Practices
We follow trunk-based development with PR-gated merges to main. Engineers are expected to:

Write tests before implementing. TDD is the expectation, not a nice-to-have.
Keep PRs small and focused; use feature flags to ship partial work incrementally
Follow conventional commit format (feat:, fix:, etc.)
Be mindful of API and schema backward-compatibility; prefer additive changes over breaking ones
Review your own diff before requesting review

Qualifications

Core Requirements

Python: Proficient in async Python, Pydantic, type hints, and FastAPI. Experience with the Strands agent SDK or a comparable agentic framework. Testing is non-optional: candidates should be comfortable with pytest, pytest- asyncio, and DeepEval for agent-specific evaluation. We test agent behavior, not just unit logic.
Prompt Engineering: Able to write and iterate on production system prompts: XML-structured, scope-enforced, with tool descriptions that guide LLM delegation reliably.
Model Context Protocol (MCP): Solid understanding of MCP and hands-on experience building or consuming MCP servers. Familiarity with Agent-to-Agent (A2A) protocol is a strong plus.
Agent Patterns: Familiar with multi-agent architectures and orchestration patterns beyond basic ReAct: supervisor/subagent delegation, parallel tool use, handoffs, and context management across agent boundaries.
Agent Evals: Able to design and run evaluation suites for agent behavior: correctness checks, scope enforcement tests, regression coverage, and systematic prompt iteration based on eval results.
Docker: Comfortable authoring Dockerfiles, multi-stage builds, and local dev environments via docker-compose / make.
PostgreSQL: Comfortable with templated INSERT/SELECT, foreign key relationships, and reading an ER diagram.
Git / Semantic Versioning: Follows conventional commit format (feat:, fix:) and a PR-based trunk workflow.

Nice to Have

Kubernetes: Working knowledge of Deployments, Services, ConfigMaps, and ServiceAccounts. Able to read and adapt K8s manifests and use kubectl for basic troubleshooting.
AWS: Working knowledge of ECR, EKS, IAM, and Bedrock (inference layer).
OAuth2 / OIDC: Conceptual understanding of Authorization Code Flow with PKCE, token exchange, and agent auth delegation.
Terraform: Beginner-level familiarity; able to make targeted changes to existing modules and interpret a plan diff.
LaunchDarkly: Experience managing feature flags or AI config overrides for environment-gated rollout.
Document Intelligence / OCR: AWS Textract or comparable pipeline experience for use cases involving structured document extraction.

AI-Native Development

We run an AI-native SDLC. AI-assisted tools (Claude Code, Cursor) are part of the standard engineering workflow at every stage: planning, implementation, review, and documentation. This is not optional or supplemental; it is how we work. Candidates should be comfortable with AI-assisted development and willing to invest in getting good at it.
Use Claude Code and Cursor actively across planning, implementation, and code review
Know when AI output is wrong and push back on it.
Help drive adoption across the team by sharing what works

What This Role Is Not
This is not an ML research or data science role. The platform team owns cluster networking, observability pipelines, auth configuration, and LLM routing, so deep expertise in those areas is not required. The job is building reliable
software on top of that infrastructure and helping others do the same.

Additional Information

About QAD:

QAD | Redzone is redefining manufacturing and supply chains through its intelligent, adaptive platform that connects people, processes, and data into a single System of Action. With three core pillars — Redzone (frontline empowerment), Adaptive Applications (the intelligent backbone), and Champion AI (Agentic AI for manufacturing) — QAD | Redzone helps manufacturers operate with Champion Pace, achieving measurable productivity, resilience, and growth in just 90 days.

QAD is committed to ensuring that every employee feels they work in an environment that values their contributions, respects their unique perspectives and provides opportunities for growth regardless of background. QAD’s DEI program is driving higher levels of diversity, equity and inclusion so that employees can bring their whole self to work.

We are an Equal Opportunity Employer and do not discriminate against any employee or applicant for employment because of race, color, sex, age, national origin, religion, sexual orientation, gender identity, status as a veteran, and basis of disability or any other federal, state or local protected class.

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