Senior Software Engineer (AI Integration)

Senior Software Engineer AI Integration

Role Overview

The Senior Software Engineer AI Integration at one of SLK Fintech client who acts as the critical link between AI and real-world implementation. This professional is responsible for designing, connecting, and delivering robust, end-to-end pipelines that integrate AI models and agentic workflows into scalable, production-grade fintech solutions.

Key Responsibilities

• Analyzes, designs, programs, debugs, and enhances software for AI-driven applications.
• Develops and maintains robust data and model pipelines, ensuring seamless integration of ML/AI components.
• Implements and manages containerized solutions using Docker and related orchestration tools (such as Kubernetes).
• Utilizes Terraform for infrastructure-as-code to provision, manage, and scale cloud resources efficiently.
• Collaborates with AI engineers to operationalize models and workflows in production environments.
• Builds and maintains CI/CD pipelines, automating testing, deployment, and monitoring processes.
• Writes clean, maintainable code and performs comprehensive testing and debugging.
• Participates in architectural design sessions and contributes to technical decision-making.
• Prepares documentation for installation, maintenance, and internal training.
• Works with cross-functional teams (Product, Sales, Client Training) to ensure successful deployment and adoption of AI-powered systems.
• Leads or contributes to project planning, estimation, and delivery for AI integration and DevOps initiatives.

Qualifications

• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field (or equivalent experience).
• Minimum 5 years of professional software engineering experience, with a focus on AI/ML integration and DevOps practices.
• programming skills in Python (and ideally, at least one other language such as Java, C++, or Go).
• Experience integrating ML/AI models into production software and building end-to-end data/model pipelines.
• Proficiency with containerization tools (Docker) and orchestration platforms (e.g., Kubernetes).
• Hands-on experience with infrastructure-as-code tools, particularly Terraform.
• Familiarity with agentic AI frameworks and orchestration tools.
• Proficiency with cloud infrastructure (AWS, GCP, Azure) and modern DevOps/MLOps practices.
• Solid understanding of software architecture, design patterns, and systems development life cycle.
• Excellent problem-solving, communication, and teamwork skills.
• Experience working in Agile or other modern software development methodologies.