Senior AI Software Engineer

About the role

You design, develop, and deploy AI-powered applications using Python to turn model experiments into production services. You integrate machine learning models, AI APIs, and intelligent algorithms into scalable backend systems while collaborating with data scientists, product managers, and software engineers to define, develop, and deliver AI-driven features. You contribute to software architecture, system design, and technical decision-making for AI-enabled products and help optimize solutions for scalability, performance, reliability, and maintainability. You own the full lifecycle of AI features from initial concept through production deployment, ensuring that solutions meet business objectives and technical standards. You translate ambiguous requirements into concrete technical specifications and implementation plans for AI capabilities. You mentor and guide junior engineers on best practices for building robust and maintainable AI systems.

Key facts

What you'll do

• Conduct risk assessments and design AI-driven features, translating requirements into scalable technical solutions while ensuring alignment with system architecture.
• Integrate machine learning models, AI APIs, and intelligent algorithms into production backend systems, connecting them with data sources and services.
• Collaborate with data scientists, product managers, and software engineers to define, develop, and deliver AI-driven features, coordinating priorities and dependencies.
• Contribute to software architecture and technical decision-making for AI-enabled products, evaluating trade-offs for performance, scalability, and maintainability.
• Develop and maintain CI/CD pipelines and containerized environments using Docker to enable reliable and repeatable deployments.
• Deploy and manage AI applications on cloud platforms such as AWS, Azure, or Google Cloud Platform (GCP), handling configuration, scaling, and monitoring.
• Optimize AI solutions for scalability, performance, reliability, and maintainability, addressing bottlenecks in inference, data flow, and resource usage.
• Build and consume RESTful APIs and integrate AI services into enterprise applications, ensuring interoperability and robustness.
• Work with LLMs, vector databases, prompt engineering, and AI agents, evaluating their applicability and limitations for product workflows.
• Stay up to date with the latest advancements in AI, machine learning, and emerging technologies, assessing opportunities to incorporate them into products and workflows.
• Analyze production metrics and logs to identify issues and opportunities for improving AI system performance and reliability.
• Collaborate with security and compliance teams to ensure AI implementations meet regulatory and organizational standards.
• Participate in code reviews and technical discussions to maintain high code quality and architectural consistency.
• Create technical documentation for AI components, APIs, and integration points to support maintenance and knowledge transfer.
• Evaluate new tools, libraries, and frameworks to determine their applicability to current and future AI initiatives.

Requirements

• Hold 5+ years of professional software development experience with a focus on integrating AI/ML solutions into production applications.
• Demonstrate strong programming skills in Python, with Java as an acceptable alternative.
• Show hands-on experience with machine learning frameworks such as TensorFlow, PyTorch, scikit-learn, or Hugging Face Transformers.
• Possess a solid understanding of machine learning concepts, data engineering, ETL processes, and model deployment.
• Have experience working with SQL and large-scale datasets to query, transform, and analyze data.
• Show familiarity with containerization using Docker, CI/CD practices, and cloud platforms such as AWS, Azure, or Google Cloud Platform.
• Have experience building or consuming RESTful APIs and integrating AI services into enterprise applications.
• Bring experience working with LLMs, vector databases, prompt engineering, or AI agents to design effective solutions.
• Maintain a strong analytical and problem-solving mindset to diagnose issues and evaluate approaches.
• Communicate excellently and collaborate effectively with cross-functional stakeholders.
• Act in a proactive, adaptable, and self-driven manner, working independently and collaboratively within cross-functional teams.
• Demonstrate genuine motivation to contribute to organizational success and alignment with its culture and values.
• Commit to following engineering best practices, coding standards, and documentation requirements consistently.
• Show willingness to learn and apply new technologies and methodologies in fast-paced environments.

Nice to have

• Show experience with modern frontend frameworks such as React, Angular, or Vue.js.
• Know MLOps tools and practices to support model lifecycle management.
• Have familiarity with Kubernetes, infrastructure as code, or microservices architectures.

Engineering methods

Use Python, Java, prompt engineering, TensorFlow, PyTorch, scikit-learn, Hugging Face Transformers, Docker, CI/CD, MLOps, SQL, RESTful APIs, LLMs, vector databases.

Relevant systems

Work with Docker, AWS, Azure, Google Cloud Platform, TensorFlow, PyTorch, scikit-learn, Hugging Face Transformers, LLMs, vector databases, and RESTful APIs.

Practical notes

Constraints are not stated. Compensation and pay bands are not stated. Team and department are not stated. Visa or clearance requirements are not stated. Onsite days are not stated.