Job Details

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AIONDATA is a software product development company specializing in artificial intelligence (AI) solutions. We help businesses innovate, optimize, and grow by leveraging AI to solve real business problems. Our services include AI strategy and consulting, custom AI product development, AI integration and modernization, and AI data management and governance. Additionally, we offer cloud strategy, digital transformation, and enterprise services such as cloud migration, digital marketing, data analytics, and custom software development.

Role Description

This is a full-time on-site role for a Director of Software Engineering, located in San Francisco, CA. The Director will provide technical leadership and strategic direction to software development teams encompassing front-end, back-end, infrastructure, and data engineering. This role involves overseeing the full software development lifecycle, ensuring the delivery of scalable, reliable, and high-performance software and data solutions. Responsibilities include guiding teams in architectural design, promoting best practices, fostering technical growth, coordinating with stakeholders, and driving technical vision.

Responsibilities:

• Provide technical leadership and mentorship to front-end, back-end, infrastructure, and data engineering teams.
• Oversee the design and architecture of complex and distributed systems, ensuring scalability and resilience.
• Drive adoption of best practices in software development, including CI/CD, testing methodologies, and code quality.
• Collaborate with product management and other stakeholders to define technical roadmaps and ensure alignment with business objectives.
• Lead and manage project delivery, ensuring timely and high-quality releases.
• Contribute to strategic technology decisions and evaluations.
• Foster a culture of innovation, continuous learning, and technical excellence within the engineering organization.
• Effectively communicate technical concepts to both technical and non-technical audiences.

Qualifications:

• Minimum of 15 years of progressive experience in software development and engineering leadership.
• Strong foundation in software development principles and practices.
• Demonstrated experience in designing and implementing distributed systems and microservices architectures.
• Proven team leadership capabilities, including experience managing and mentoring diverse engineering teams (front-end, back-end, infrastructure, data).
• Solid understanding of project management methodologies (Agile, Scrum, etc.) and tools.
• Proficiency in software design patterns and architectural principles.
• Excellent problem-solving and analytical skills.
• Strong communication (written and verbal) and interpersonal skills.
• Ability to work effectively and collaboratively in an on-site team environment.
• Experience with cloud platforms, preferably AWS.
• Familiarity with data engineering concepts and technologies, such as Snowflake and Databricks.
• Exposure to front-end technologies like React.js and back-end development.
• Understanding of MLOps principles and practices is a plus.
• Experience with MDM (Master Data Management) concepts is a plus.
• Master's degree in Computer Science, Engineering, or a related field is required.

Technology:

• Cloud Platforms: Deep understanding of at least one major cloud provider (e.g., AWS, Azure, GCP), including infrastructure as code (e.g., Terraform, CloudFormation), networking, and security best practices. Specific experience with AWS is a plus.
• Data Engineering & Analytics: Familiarity with modern data warehousing and processing technologies such as Snowflake and Databricks. Understanding of ETL/ELT processes, data modeling, and data governance. Exposure to MDM concepts is beneficial.
• Back-end Technologies: Experience with various back-end languages and frameworks (e.g., Python, Java, Node.js, Go) and architectural patterns (microservices, API design).
• Front-end Technologies: Understanding of modern front-end frameworks and libraries, particularly React.js, and the principles of UI/UX development.
• DevOps & Infrastructure: Knowledge of CI/CD pipelines, containerization (e.g., Docker, Kubernetes), and monitoring/logging tools.
• AI/ML Operations (MLOps): Familiarity with the principles and practices of deploying and managing machine learning models in production.

Seniority level

• Seniority level

  Director

Employment type

• Employment type

  Full-time

Job function

• Job function

  Engineering and Information Technology

• Industries

  Technology, Information and Internet

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