Data Engineer (AI Experienced)

Groupe SEGULA Technologies SA; Mexico; On-site; PHP 7,353,000 - 11,029,000; Full time

Job summary

Groupe SEGULA Technologies SA is seeking a senior AI/ML leader to design, build, and deploy production-grade AI systems for cost estimation, optimization, and decision support. You will own end-to-end pipelines and collaborate with cost engineers, data engineers, and business stakeholders.

You will drive MLOps, implement LLM-powered applications, and ensure governance, security, and responsible AI across enterprise platforms such as Databricks, Snowflake, Azure, and AWS.

Responsibilities

• Design, develop, and deploy scalable AI/ML models tailored for cost engineering.
• Build and maintain end-to-end ML pipelines (data ingestion, feature engineering, model training, validation, deployment).
• Implement model monitoring, automated retraining, and continuous performance optimization in production environments.
• Apply robust MLOps practices for versioning, testing, monitoring, lifecycle management, and CI/CD pipelines.
• Design, develop, and deploy production-ready LLM-powered applications (copilots, chatbots, knowledge assistants) for cost engineering use cases.
• Implement RAG architectures, vector databases, and multi-agent orchestration utilizing enterprise data sources.
• Collaborate with data engineers to integrate AI solutions into enterprise data platforms (Databricks, Snowflake, Palantir Foundry, Azure, AWS, ServiceNow, Power Platform).
• Work across structured and unstructured data assets (BOMs, supplier data, costing data, engineering documents).
• Integrate AI capabilities directly into cost engineering tools, executive dashboards, and operational business workflows.
• Collaborate with onshore/offshore cross-functional teams and translate domain problems into actionable AI/ML solutions.
• Partner with business stakeholders to define requirements, success metrics, and KPIs.
• Provide technical leadership, mentor junior engineers, and promote AI capability building.
• Ensure AI solutions comply with enterprise data governance, security, traceability, and responsible AI standards.

Job description

This role focuses on building production-grade AI systems that enable intelligent cost estimation, optimization, analysis, and decision support. This includes building end-to-end AI pipelines, LLM-powered applications, and AI-assisted workflows integrated into enterprise platforms. Working at the intersection of cost engineering, data platforms, and modern AI, you will partner closely with cost engineers, data engineers, and business stakeholders in a hands-on technical capacity.

Key Responsibilities

1. AI/ML Solution Development & MLOps

• Design, develop, and deploy scalable AI/ML models tailored for cost engineering.
• Build and maintain end-to-end ML pipelines (data ingestion, feature engineering, model training, validation, deployment).
• Implement model monitoring, automated retraining, and continuous performance optimization in production environments.
• Apply robust MLOps practices for versioning, testing, monitoring, lifecycle management, and CI/CD pipelines.

2. Generative AI & LLM Applications

• Design, develop, and deploy production-ready LLM-powered applications (copilots, chatbots, knowledge assistants) for cost engineering use cases.
• Implement RAG architectures, vector databases, and multi-agent orchestration utilizing enterprise data sources.

3. Data & Platform Integration

• Collaborate with data engineers to integrate AI solutions into enterprise data platforms (Databricks, Snowflake, Palantir Foundry, Azure, AWS, ServiceNow, Power Platform).
• Work across structured and unstructured data assets (BOMs, supplier data, costing data, engineering documents).
• Integrate AI capabilities directly into cost engineering tools, executive dashboards, and operational business workflows.

4. Cross-functional Leadership & Governance

• Collaborate with onshore/offshore cross-functional teams and translate domain problems into actionable AI/ML solutions.
• Partner with business stakeholders to define requirements, success metrics, and KPIs.
• Provide technical leadership, mentor junior engineers, and promote AI capability building.
• Ensure AI solutions comply with enterprise data governance, security, traceability, and responsible AI standards.