Who We Are

At MAS Global Consulting, we are a premium digital engineering partner trusted by innovative startups and Fortune 100 companies, delivering high-impact solutions through agile delivery, deep technical expertise, and a strong people-first culture across the Americas.

We build long-term partnerships, not just software. MAS means “more” in Spanish, and that mindset drives our commitment to greater opportunity, inclusion, and impact.

Founded by a Latina engineer from Medellín and headquartered in Tampa, Florida, MAS Global Consulting is a 100% Hispanic and woman-owned company, recognized as a Great Place to Work and one of the Fastest-Growing Companies in the US.

Hands-On Technical Delivery (40%)

• GenAI & Agentic Systems: Design and deploy production GenAI systems including RAG architectures, fine-tuning (LoRA/QLoRA), multi-agent orchestration, and agentic workflows using frameworks like LangGraph, LlamaIndex, or Agno.
• Production Infrastructure: Own the deployment lifecycle end-to-end. Containerize with Docker, orchestrate on Kubernetes (EKS/GKE/AKS), implement GitOps pipelines (ArgoCD, Flux), and manage model serving infrastructure (vLLM, TGI, Triton).
• MLOps & Observability: Implement CI/CD for ML pipelines, model versioning, A/B testing, drift detection, and monitoring using tools like MLflow, Langfuse, Opik, W&B, Prometheus, and Grafana.
• Data & ML Foundations: Architect data pipelines and apply classical ML and deep learning models where appropriate. You should be comfortable across the full ML spectrum, from feature engineering to transformer fine-tuning.
• Engineering Excellence: Conduct code reviews, architectural reviews, and technical mentorship. Define best practices for AI-assisted development workflows (Cursor, Claude Code, GitHub Copilot). Enforce coding standards and documentation requirements.

Presales & Business Development (30%)

• Lead technical presales: discovery calls, solution architecture, POC scoping, and proposal development.
• Serve as the trusted AI advisor for clients, translating business problems into feasible AI solutions with clear ROI.
• Develop compelling technical proposals and architecture diagrams that win deals.
• Build relationships with C-level executives and technical decision-makers.
• Identify upsell and cross-sell opportunities within existing accounts.
• Represent MAS Global at industry events and conferences as a recognized AI thought leader.

AI Strategy & Thought Leadership (20%)

• Define and evolve the company's AI service offerings aligned with market demand.
• Build reusable AI assets, accelerators, and frameworks that reduce delivery time.
• Evaluate build-vs-buy decisions for AI tools, platforms, and infrastructure.
• Create thought leadership content: blog posts, whitepapers, case studies, and conference talks.
• Design evaluation frameworks for GenAI systems (LLM-as-a-Judge, automated benchmarking, human eval protocols).

Team Building & Leadership (10%)

• Recruit, mentor, and grow a high-performing AI engineering team.
• Design career paths and skill matrices for AI/ML engineers.
• Foster a culture of continuous learning, experimentation, and knowledge sharing.
• Conduct technical interviews and assess AI talent.

Education & Experience

• Bachelor's or Master's in Computer Science, Data Science, Mathematics, or related quantitative field.
• 8+ years of professional experience in data engineering, ML, and/or AI, with at least 2 years in a leadership or principal-level role.
• 3+ years deploying production ML/AI systems at scale across multiple industries.
• 2+ years of hands-on experience with Generative AI / LLM applications in production environments.
• Demonstrated experience in presales, technical sales, or client-facing consulting roles.

Generative AI & Agentic Systems

• Production experience with LLM APIs (OpenAI, Anthropic, Google) and open-source models.
• Solid experience designing RAG systems: chunking strategies, embedding models, vector databases, hybrid search, reranking, and evaluation.
• Hands-on experience with agent frameworks (LangChain, LangGraph, LlamaIndex, Agno, ADK, OpenAI Agents SDK).
• Ability to design and deploy multi-agent architectures in production, including tool orchestration, human-in-the-loop patterns, and integration with emerging standards (MCP, A2A).
• Proficiency in prompt engineering: system prompts, few-shot, chain-of-thought, structured outputs, and guardrails.
• Knowledge of fine-tuning techniques (LoRA, QLoRA, RLHF, DPO) and when to apply each.
• Experience building evals for GenAI: LLM-as-a-Judge, RAGAS, custom metrics, human evaluation loops.

Production Infrastructure & MLOps (Critical)

• Kubernetes in production: Deep experience deploying and managing ML/AI workloads on Kubernetes (EKS, GKE, or AKS). You should be comfortable with Helm, Kustomize, namespaces, resource limits, GPU scheduling, and debugging pods.
• GitOps & CI/CD: Hands-on experience with ArgoCD, Flux, or equivalent GitOps tools. Ability to design and maintain CI/CD pipelines for ML systems (GitHub Actions, GitLab CI, or cloud-native).
• Model Serving & Inference: Experience deploying models with vLLM, TGI, Triton, or equivalent serving infrastructure. Understanding of autoscaling, batching strategies, and latency optimization.
• Observability & Monitoring: Production monitoring with Prometheus, Grafana, Datadog, or equivalent. Experience setting up alerts, SLOs, and dashboards for ML systems.
• Infrastructure-as-Code: Terraform, Pulumi, or CloudFormation for reproducible infrastructure.
• Containerization: Expert-level Docker skills. Multi-stage builds, optimized images, security scanning.

Cloud Platforms

• Deep experience with at least two major cloud platforms: AWS (SageMaker, Bedrock), Azure (Azure ML, Azure AI Foundry), or GCP (Vertex AI, BigQuery ML).
• Understanding of serverless architectures (Lambda, Cloud Functions, Cloud Run) and when to use them vs. Kubernetes.

Data & Classical ML Foundations

• Expert-level proficiency in Python and SQL.
• Solid foundation in classical ML algorithms, feature engineering, and experiment tracking.
• Experience with cloud data platforms (Snowflake, Databricks, BigQuery) and pipeline tools (dbt, Spark, Airflow).
• Understanding of deep learning frameworks (PyTorch preferred) and transformer architectures.

Soft Skills & Leadership

• Exceptional communication: ability to explain complex AI concepts to non-technical executives.
• Strong presales instincts: understanding of sales cycles, proposal writing, and client relationship management.
• Executive presence with C-level stakeholders.
• Fluent English (C1+). Spanish is a strong plus.
• Entrepreneurial mindset: comfortable building from scratch and operating with ambiguity.

Advanced Credentials

• PhD in Computer Science, Machine Learning, Statistics, or related field.
• Published papers in top-tier AI conferences (NeurIPS, ICML, ICLR, ACL, CVPR).
• Cloud architect certifications (AWS, Azure, GCP) or data platform certifications (Databricks, Snowflake).

Cutting-Edge Experience

• Experience with reasoning models and test-time compute scaling.
• Multimodal AI: vision-language models, audio/speech, video understanding.
• AI governance frameworks, responsible AI practices, and EU AI Act compliance.
• Graph databases (Neo4j) and GraphRAG patterns.
• FinOps for AI: GPU cost optimization, inference cost management, token economics.
• Blue/green and canary deployment strategies for ML models.
• Service mesh experience (Istio, Linkerd) for ML microservices.

Business & Leadership

• Previous experience as AI/ML practice lead at a consulting firm.
• Track record building and scaling AI teams (5+ people) from scratch.
• Conference speaking experience at industry events.
• Open-source contributions to AI/ML projects.
• Experience in regulated industries: financial services, healthcare, or government.