Sr AI Integration Engineer

Role 3 : Sr AI Integration Engineer: 5-8 years –1 Role (Budget – to be discussed)

Interview round : 2 SLK and 1 Client

Role Overview

The Senior Software Engineer – AI Integration at one of our Fintech client will 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.