AI Software Engineer-Planta Ford Irapuato

Efds

Irapuato, Mexico9h ago

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Key details

Location: Irapuato, Mexico
Work style: Onsite
Level: Mid level
Experience: 3–6 yrs
Function: Software engineering
Focus area: AI / ML
Posted: 9h ago

Skills

agileai agentsai/mlapplication developmentartificial intelligenceawsazurebusiness intelligenceci/cdcloud computingcode reviewsconcurrencycost trackingdata engineeringdata integrationdata pipelinesdata qualitydeep learningdesign patternsdevopsembeddingsenterprise architectureevent-driven architectureexperiment trackinggcpgenerative aigitgraphqlgrpckubernetesllmllmopsmachine learningmlopsmodel deploymentmodel evaluationmodel fine-tuningobject-oriented programmingprompt engineeringpythonragregression analysisrestroot cause analysissecure codingsoftware architecturestate managementvector databasesversion controlworkflow orchestration

About the role

The AI Software Engineer is responsible for designing, developing, implementing, and

maintaining AI-enabled software applications that support business and manufacturing

operations. The position works closely with AI Architects, Data Engineers, Manufacturing

Teams, and Digital Transformation leaders to build intelligent solutions using Machine

Learning, Generative AI, cloud technologies, and enterprise data platforms.

This role contributes to the development of scalable AI software products, data pipelines,

APIs, and predictive models while supporting deployment, monitoring, and continuous

improvement activities. The AI Software Engineer serves as a key contributor to Industry 5.0

and Industry 6.0 initiatives, helping transform operational data into actionable business

insights through innovative AI-driven solutions.

Main Responsibilities

AI Software Development

 Develop AI-enabled software applications and intelligent business solutions.

 Build and maintain Machine Learning and data processing pipelines.

 Develop APIs and backend services that support AI products and digital

applications.

 Participate in software architecture implementation following engineering

standards and best practices.

AI Model Development & Deployment

 Support the development, training, testing, and deployment of Machine Learning

models.

 Assist in implementing Generative AI solutions and Large Language Model (LLM) use

cases.

 Monitor model performance, accuracy, and reliability in production environments.

 Support MLOps processes including model deployment, monitoring, and lifecycle

management.

Data Integration & Analytics

 Integrate manufacturing, operational, and enterprise data sources.

 Perform data preprocessing, cleansing, validation, and transformation activities.

 Maintain data quality, consistency, and accessibility across platforms.

 Support business intelligence, analytics, and dashboard development initiatives.

Collaboration & Continuous Improvement

 Collaborate with AI Architects, Data Engineers, Manufacturing Teams, and Business

Stakeholders.

 Participate in Agile ceremonies, sprint planning, and technical reviews.

 Contribute to digital transformation and continuous improvement initiatives.

 Support troubleshooting, incident resolution, and software maintenance activities.

Innovation

 Research emerging AI technologies, frameworks, and industry trends.

 Support proof-of-concept (PoC) and pilot projects involving AI and advanced

analytics.

 Recommend software, data, and process improvements that enhance operational

performance.

Software Engineering: Expert in modern programming languages, clean code practices, SOLID principles, design patterns, concurrency, refactoring, technical debt management, Git workflows, and large-scale system development.
AI Application Development: Builds solutions using LLMs, prompt engineering, structured outputs, tool/function calling, RAG architectures, embeddings, vector databases, and AI agent frameworks.
LLM Engineering: Understands transformer architectures, tokenization, context management, model selection, inference optimization, fine-tuning techniques, and deployment of both open-source and commercial models.
AI Agents & Automation: Develops intelligent agents with memory, state management, tool integration, workflow orchestration, human-in-the-loop controls, security boundaries, and enterprise integrations.
Machine Learning & Data Engineering: Applies practical ML techniques, develops training and inference pipelines, manages structured and unstructured data, and creates reliable data processing ecosystems.
API & System Integration: Connects applications through REST, GraphQL, gRPC, event-driven architectures, messaging platforms, authentication systems, and enterprise services.
Cloud & Platform Engineering: Designs and deploys solutions on AWS, Azure, or GCP using containers, Kubernetes, serverless services, managed databases, monitoring, and security controls.
MLOps & LLMOps: Implements CI/CD pipelines, model lifecycle management, experiment tracking, automated evaluations, monitoring, version control, and controlled releases.
Testing & Evaluation: Ensures quality through unit, integration, end-to-end, performance, AI workflow, and model evaluation testing, including hallucination detection and regression analysis.
Security & Governance: Applies secure coding practices, OWASP standards, agent security controls, prompt injection protection, data privacy safeguards, and access management.
Observability & Performance: Monitors systems using logging, tracing, metrics, cost tracking, and optimization techniques to maximize reliability and efficiency.
System Design & Architecture Support: Designs scalable components, evaluates technical trade-offs, identifies risks, documents decisions, and aligns implementations with enterprise architecture.
Troubleshooting & Operations: Diagnoses and resolves application, infrastructure, model, and production issues through systematic root cause analysis.
Technical Leadership: Mentors developers, performs code reviews, owns critical components, leads technical initiatives, communicates risks effectively, and drives engineering excellence.

Education

 Bachelor’s Degree in Computer Science, Software Engineering, Information

Technology, Artificial Intelligence, Data Science, or related field.

 Certifications in AI, Cloud Technologies, Data Engineering, or Software

Development are a plus.

Experience

 3+ years of experience in software development, Artificial Intelligence, Machine

Learning, or related technical roles.

 Experience developing cloud-based applications and enterprise software solutions.

 Experience working with Python and AI/ML frameworks.

 Experience integrating APIs, databases, and cloud services.

 Basic understanding of DevOps and MLOps practices.

 Experience in manufacturing, Industry 5.0, or smart factory environments is

preferred.

Technical Skills

Artificial Intelligence & Machine Learning

 Machine Learning Model Development.

 Deep Learning Fundamentals.

 Generative AI Concepts.

 Large Language Models (LLMs).

 Model Training and Evaluation.

 AI Solution Deployment and Monitoring.

Programming & Software Development

 Python Development.

 Object-Oriented Programming.