> Markdown version of [/jobs/ext/4586024-senior-ai-ml-solutions-architect-mysql](https://www.wearedevelopers.com/jobs/ext/4586024-senior-ai-ml-solutions-architect-mysql). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

---

# Senior AI/ML Solutions Architect - MySQL

- **Company:** HCL Technologies
- **Location:** Palo Alto, CA, United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Airflow, Amazon Web Services, Data Analysis, Cloud Computing, Information Engineering, Extract Transform Load (ETL), Distributed Systems, Electronic Data Interchange (EDI), Python (Programming Language), PostgreSQL, Machine Learning, MySQL, Natural Language Processing, RabbitMQ, Tensorflow, Azure Machine Learning, Software Engineering, SQL Databases, Data Storage Management, Data Ingestion, Pytorch, Apache Spark, Deep Learning, Spark Mllib, Containerization, Scikit Learn, Xgboost, Apache Kafka, Machine Learning Operations, Video Streaming, Restful APIs, Data Pipelines, Databricks
- **Published:** November 5, 2026
- **Apply:** https://www.careerbuilder.com/job-details/senior-ai-ml-solutions-architect-mysql-palo-alto-ca--d1f72e75-96c8-470a-82f7-409b0ca39446

## About the Role

Artificial Intelligence (AI), MySQL, Artificial Intelligence (AI), MySQL, Industry Standards, Business Transformation, Software Engineering, Machine Learning, Cloud Computing, Mentoring, 1. Expert Proficiency In Ai/Ml Model Development Using Python, Tensorflow, Pytorch, Scikitlearn, And Related Frameworks.

 2. Excellent Knowledge Of Data Engineering And Distributed Computing With Apache Spark, Kafka, And Airflow.
 3. Advanced Skills In Sql, Mysql, Postgresql, And Data Pipeline Design For Largescale Analytics.
 4. Solid Experience With Nlp, Deep Learning, Time Series Forecasting, And Classical Ml Algorithms.
 5. Strong Understanding Of Cloudbased Ml Platforms Such As Databricks And Integration Of Realtime Streaming Technologies.
 6. Excellent Ability To Translate Business Objectives Into Technical Architectures And Actionable Ml Solutions.
 7. Expert Mentoring And Technical Leadership Capabilities Within Ai/Ml Teams.

Other Requirements

 1. Recommended certifications: TensorFlow Developer Certificate
 2. AWS Certified Machine Learning � Specialty
 3. Databricks Certified Data Engineer Professional (optional but valuable)

## Description

This role is responsible for architecting and designing advanced AI and machine learning solutions that drive business transformation and innovation. The position leverages deep expertise in data engineering, scalable ML systems, and cloud-native technologies to deliver robust, secure, and high-performance platforms. The individual provides strategic technical direction, mentors team members, and ensures alignment of solutions with organizational objectives and industry standards., 1. Architect and design scalable machine learning pipelines using Python, TensorFlow, PyTorch, and Apache Spark, ensuring optimal performance, reliability, and security.

 2. Develop and implement advanced ML models for NLP, deep learning, and time series forecasting using scikit-learn, XGBoost, and Spark Mllib, aligning model outputs with business requirements.
 3. Integrate real-time data streaming solutions using Apache Kafka and RabbitMQ to support high-throughput ML applications and seamless data ingestion.
 4. Optimize data storage and retrieval by designing robust schemas and ETL workflows in MySQL, PostgreSQL, and DataBricks, supporting large-scale analytics and model training.
 5. Lead technology strategy by evaluating emerging AI/ML tools and frameworks, recommending adoption to enhance solution quality and future-proof architectures.
 6. Mentor and guide team members in best practices for ML model development, data engineering, and deployment using Python, SQL, and Apache Airflow.
 7. Collaborate with stakeholders to gather requirements, translate business needs into technical specifications, and deliver AI-driven solutions that meet client expectations.
 8. Architect and implement RESTful API integrations to enable seamless communication between AI/ML components and external systems, ensuring scalable, secure, and efficient data exchange across diverse enterprise environments.