AI Architect

SMX Services & Consulting, Inc.; United States; Remote; USD 170,000 - 230,000; Full time

Job summary

SMX Services & Consulting, Inc. seeks an experienced AI Architect to design, develop, and deploy scalable AI/ML solutions on Microsoft Azure. You will lead end-to-end projects, from architecture to production deployment and monitoring.

The role requires hands-on expertise across NLP, Deep Learning, Generative AI, PySpark, Spark, Python, and MLOps. Strong leadership and collaboration with clients and stakeholders are essential.

Qualifications

• Proven experience architecting and implementing enterprise AI/ML solutions in a senior role.
• At least one project architecting and deploying an AI solution on Microsoft Azure.
• Hands-on experience with end-to-end AI/ML model lifecycle: training, validation, production deployment, monitoring.

Responsibilities

• Design and implement scalable, secure, production-ready AI/ML architectures on Microsoft Azure.
• Collaborate with clients and stakeholders to translate business requirements into AI solutions.
• Lead end-to-end AI projects from solution design to deployment and production support.
• Develop and optimize ML, DL, NLP, and Generative AI solutions.
• Build data pipelines with Python, PySpark, Spark, and SQL.
• Choose AI algorithms, frameworks,Cloud services, and infra for deployments.
• Create prototypes, POCs, and MVPs.
• Implement MLOps with versioning, automated deployment, monitoring, retraining.

Skills

Python

PySpark

SQL

NLP

TensorFlow

Keras

PyTorch

Docker

Kubernetes

MLflow

DVC

Azure

Azure Databricks

AKS

Flask

Django

Tools

Azure

Azure Databricks

AKS

MLflow

DVC

Flask

Django

Spark

Job description

Contract Duration: 12 Months (Renewable)

Employment Type: Full-Time Contract

Job Summary

We are seeking an experienced AI Architect to design, develop, and deploy scalable, production-ready Artificial Intelligence (AI) and Machine Learning (ML) solutions using Microsoft Azure.

The ideal candidate will have strong hands-on experience with AI/ML architecture, Deep Learning, Natural Language Processing (NLP), Generative AI, Python, PySpark, Apache Spark, and MLOps.

This role requires technical leadership and practical experience managing the complete AI model lifecycle, from requirements gathering and architecture design through model development, training, deployment, monitoring, and ongoing optimization.

Key Responsibilities

• Design and implement scalable, secure, and production-ready AI/ML architectures on Microsoft Azure.
• Collaborate with clients and business stakeholders to translate business requirements into AI solutions.
• Lead end-to-end AI projects, including solution design, model development, training, validation, deployment, and production support.
• Develop and optimize Machine Learning, Deep Learning, NLP, and Generative AI solutions.
• Design data ingestion, preprocessing, transformation, and feature engineering pipelines using Python, PySpark, Apache Spark, and SQL.
• Select and implement appropriate AI algorithms, frameworks, cloud services, and infrastructure.
• Develop prototypes, proofs of concept (POCs), and minimum viable products (MVPs).
• Implement MLOps practices, including model versioning, automated deployment, monitoring, retraining, and lifecycle management.
• Integrate AI models with enterprise applications, APIs, databases, and existing business systems.
• Implement CI/CD workflows and containerized model deployments using Docker and Kubernetes.
• Monitor production models for performance degradation, data drift, anomalies, and operational issues.
• Optimize AI solutions for scalability, performance, reliability, security, and cost efficiency.
• Provide technical leadership, architectural guidance, documentation, and mentorship to engineering teams.

Required Qualifications

• Proven experience designing and implementing enterprise AI/ML solutions in a senior engineering or architecture role.
• At least one significant project architecting and deploying an AI solution on Microsoft Azure.
• Hands-on experience managing the complete lifecycle of at least two AI/ML models, including training, validation, production deployment, monitoring, and maintenance.
• Strong hands-on programming experience with Python, PySpark, and SQL.
• Advanced experience with Apache Spark and large-scale data processing.
• Strong knowledge of Machine Learning and Deep Learning frameworks, including TensorFlow, Keras, and PyTorch.
• Experience with ML libraries such as scikit-learn, XGBoost, and LightGBM.
• Experience designing and deploying NLP and GPT-based AI solutions.
• Practical knowledge of MLOps, model versioning, monitoring, and automation, including tools such as MLflow or DVC.
• Experience with Docker and Kubernetes for model deployment and orchestration.
• Experience developing APIs and deploying AI models using Flask or Django.
• Strong understanding of AI architecture, cloud infrastructure, data pipelines, model optimization, and production scalability.
• Experience collaborating with data scientists, software engineers, cloud teams, and business stakeholders.
• Strong analytical, problem-solving, communication, and technical leadership skills.

Preferred Qualifications

• Experience with Azure Machine Learning, Azure OpenAI Service, Azure AI Services, Azure Databricks, and AKS.
• Experience with Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), embeddings, vector databases, and model fine-tuning.
• Experience implementing automated ML pipelines and CI/CD workflows using Azure DevOps or GitHub Actions.
• Knowledge of cloud security, AI governance, data privacy, and responsible AI practices.
• Experience with enterprise-scale AI deployments and cloud cost optimization.

Core Technical Skills

Programming: Python, PySpark, SQL

Machine Learning: scikit-learn, XGBoost, LightGBM

Deep Learning: TensorFlow, Keras, PyTorch

Generative AI: GPT, LLMs, NLP

API Development: Flask, Django

Candidates must be able to demonstrate:

• At least one production AI/ML implementation on Microsoft Azure where they held architecture responsibilities.
• At least two AI/ML models personally managed through the complete lifecycle, including production deployment and monitoring.
• Strong hands-on experience with Python, PySpark, Spark, and Deep Learning frameworks.
• Practical experience implementing MLOps and production AI architectures.
• Ability to clearly explain their individual contributions, technical decisions, project scope, and business outcomes.

Note: This is a hands‑on AI Architect position requiring proven production implementation experience, not exclusively theoretical, research, or advisory experience.