Artificial Intelligence Engineer

Roles and Responsibilities:

3 – 5 years of product development experience for large scale systems with high volume and high-performance requirements.

About the Role

AI Engineer with a blend of AI deployment, MLOps, software integration, and AI infrastructure skills, the ideal candidate should have:

Responsibilities

• Strong AI/ML Engineering Skills
• Hands-on experience in model deployment, integration, optimization, and serving AI models in production.
• Ability to integrate AI models into lending workflows and APIs.
• Expertise in LLM prompt engineering, calling tools, RAG, and structured outputs.
• Implement hallucination minimization techniques and create continuous feedback loops for model improvement.

• ML Ops & Cloud Deployment Expertise
• Knowledge of CI/CD pipelines for AI models.
• Knowledge in containerization (Docker, Kubernetes).
• Knowledge of security and compliance.
• Implement AI guardrails, secure data protection protocols, and ensure compliance with financial data standards.
• Knowledge of cloud-based AI services (AWS).
• AI Software & API Integration
• Expert in Python / TypeScript programming.
• Building APIs to serve AI models in real-time decision-making for lending and risk assessment.
• Experience with serverless AI architectures and event-driven workflows.
• Experience with Vector DB and RDBMS / NoSQL.
• AI Infrastructure & Scaling
• Knowledge of AI workloads efficiently, optimizing for low latency and high availability.
• Experience with GPU acceleration and distributed computing frameworks.

• Preferably from a product-based company.
• Minimum 60% throughout academics, including highest qualification.

Required Skills

• Strong programming skills in Python / TypeScript, and API development.
• Hands-on experience with MLOps tools, containerization, and cloud platforms.
• Knowledge of AI model deployment, scaling, and performance optimization.

Preferred Skills

• Experience with AI model deployment, scaling, and performance optimization.