Senior AI Engineer

Architect and implement AI-powered features end-to-end, from model integration and prompt engineering to deployment, monitoring, and iteration

Integrate LLM APIs, ML models, and intelligent automation pipelines into scalable, production-grade backend systems

Fine-tune and optimize models for performance, reliability, and cost efficiency in live environments

Design and build robust APIs, event-driven services, and third-party integrations that support AI-enabled workflows

Collaborate with Product to translate business requirements into technical AI solutions, defining scope, complexity, and dependencies

Build and maintain data pipelines and MLOps workflows to support model deployment and lifecycle management

Contribute to system architecture decisions, integration patterns, and reusable AI frameworks across the platform

Partner with data scientists and engineers to ensure smooth, scalable model deployments

Produce clear technical documentation, architecture diagrams, data flows, API specs, and AI integration patterns

Lead code reviews, enforce best practices in code quality and testing, and mentor engineers across teams

Troubleshoot complex production issues across distributed and AI-integrated systems

Stay ahead of industry trends in AI/ML tooling, frameworks, and practices

5+ years of experience building scalable, production-grade software systems

2+ years of hands-on AI/ML engineering experience, including deploying models into production environments

Bachelor's degree in Computer Science, Data Science, AI, or a related field

Strong expertise in Python, with hands-on experience using frameworks such as TensorFlow, PyTorch, or Scikit-learn

Solid backend engineering background, API development, microservices, distributed systems, and event-driven architectures

Experience integrating LLM APIs and building AI-driven features into web or platform applications

Strong proficiency in JavaScript/TypeScript and Node.js, with exposure to GraphQL

Deep experience building and consuming RESTful APIs and distributed services

Proficiency with SQL and hands-on experience with cloud platforms, GCP or AWS (e.g., S3, Lambda, RDS, EC2)

Familiarity with MLOps best practices and tools for model monitoring, versioning, and deployment

Strong understanding of software design patterns, system architecture, and data flow design

Excellent communication skills with the ability to articulate complex technical concepts clearly

Experience working in Agile/Kanban environments with a strong grasp of the full SDLC

Experience with data platforms, analytics systems, or ETL pipelines

Familiarity with real-time data processing and messaging systems such as Kafka or SQS

Exposure to tax, fintech, or compliance-related product development

Experience designing multi-tenant or enterprise-grade SaaS platforms

Background in integration-heavy environments such as ERP, financial systems, or external data providers