Lead AI Integration Engineer

3Pillar Global

Lead AI Integration Engineer at 3Pillar developing AI solutions and intelligent backend services. Engage in complex AI/ML projects with global clients across industries.

Posted 7/2/2026full-timeRemote • 🇲🇽 MexicoSeniorWebsite

Tech Stack

Tools & technologies

AzureJavaPythonSDLC

About the role

Key responsibilities & impact

Design, build, and deploy intelligent backend services using Python and Azure AI Foundry.
Consume Force Account outputs by API, ensuring the system can accurately display and act on data classification and extraction.
Engineer and optimize Retrieval-Augmented Generation (RAG) pipelines over Standard Operating Procedures (SOPs) to provide contextual, accurate, and actionable insights to users.
Deliver assistive intelligence directly on the case, seamlessly integrating AI outputs into the end-user's workflow.
Design and strictly enforce a redact-before-model discipline, ensuring that all Personally Identifiable Information (PII) is stripped from payloads before data ever reaches a foundational model.
Collaborate with product, design, and engineering teams to shape AI features and translate business goals into technical execution.
Proactively identify AI-driven automation and product improvement opportunities, building prototypes to prove value.
Mentor junior engineers and champion secure, responsible AI coding practices within your team.
Troubleshoot AI-specific technical challenges, including prompt injection risks, model latency, and data leakage.
Guide team members on model selection, evaluation, fine-tuning, and Gen AI usage.
Actively evaluate emerging LLM tooling, agentic frameworks, and open-source developments for applicability to client engagements.
Own the architecture of the team's agentic and LLM systems — agent orchestration, tool integration (e.g., Model Context Protocol), retrieval, and human-in-the-loop design.
Design and implement evaluation frameworks for LLM and agentic systems — covering correctness, boundary, and intent checks — as a standard part of delivery.
Drive adoption of the agentic SDLC across the team: select and roll out AI engineering tools, define usage and governance standards, and measure their impact on delivery velocity and quality.
Establish responsible-AI and evaluation frameworks for agentic systems — guardrails, safety testing, human oversight, and model governance.

Requirements

What you’ll need

A minimum of 8+ years of experience in software engineering, with at least 2+ years of hands-on experience integrating LLMs and building AI-driven applications.
Demonstrated experience architecting and deploying LLM-based or agentic systems in production, including prompt design, retrieval architecture, and tool/function calling (e.g., MCP)
Hands-on experience with leading LLMs and APIs, including OpenAI, Google Gemini, and Anthropic Claude.
Experience evaluating and selecting open-source and proprietary LLMs (e.g., OpenAI via Azure AI Foundry) based on accuracy, latency, scalability, cost, and overall performance for production AI solutions.
Knowledge of vector stores, embeddings (LangChain, LlamaIndex, etc.).
High level of English proficiency required to interact with a globally-based development team and client stakeholders.
Deep expertise in Python for backend services and AI integration (replacing traditional Java/C# requirements).
Demonstrated, hands-on experience utilizing Azure AI Foundry to build, deploy, and manage AI models and agents.
Proven track record of building and managing complex API integrations for data ingestion, extraction, and classification.
Strong understanding of security engineering, specifically implementing PII redaction, anonymization pipelines, and secure data handling in AI workflows.
Experience delivering well-tested, scalable, secure, and performant enterprise-level systems that achieve client business outcomes.
Strong track record of working within Agile software development frameworks.
Proficiency with AI-assisted development tools (Claude Code, Cursor, GitHub Copilot) as a core daily practice, including pipeline construction, test generation, and documentation.

Benefits

Comp & perks

Medical Insurance benefits as per company policy.
Life Insurance as per company policy
15 days of paid vacation plus additional days based on tenure
Vacation bonus as per local law
Sick leave and paid holidays as per local law
Profit sharing as per local law
Paternity and maternity leave as per as per local law
Marriage, bereavement and graduation leaves as per company policy
Christmas bonuses as per local law
Work from home allowance as per company policy
Punctuality and attendance bonuses as per company policy
Food tickets as per company policy
Direct deposit of payroll.
Paid professional certifications

ATS Keywords

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Hard Skills & Tools

Software EngineeringAI-Driven ApplicationsPrompt DesignRetrieval ArchitectureData ClassificationAnonymization PipelinesAgile MethodologiesVector StoresEmbeddingsModel Evaluation

Soft Skills

MentoringCollaborationProblem-SolvingCommunication