AI Engineer

Job Description

The Role

As an AI Engineer at you’ll design, build, and deploy the machine learning and AI systems that power our cash forecasting, anomaly detection, and decision-intelligence capabilities. You’ll work closely with product, data, and treasury domain experts to turn financial data — from banks, ERPs, and payment systems — into real-time, trustworthy AI-driven insights for CFOs.

What You’ll Do

• Design, develop, and deploy ML/AI models for cash flow forecasting, transaction categorization, and financial anomaly detection
• Build and maintain data pipelines that ingest and normalize data from banks, ERPs, and payment systems
• Fine-tune and optimize models (including LLMs) for performance, latency, and cost in production
• Build RAG pipelines and agentic workflows that help finance teams query and act on financial data conversationally
• Integrate AI/ML models into the core platform via APIs and services
• Monitor deployed models for drift, performance degradation, and reliability, given the high accuracy bar of financial data
• Apply statistical methods to validate model performance, design experiments (A/B tests), and interpret forecasting accuracy
• Implement MLOps best practices: CI/CD for models, versioning, testing, and monitoring
• Partner with product and treasury domain experts to align model outputs with real CFO/finance-team workflows
• Help shape and scale AutoCash’s India engineering hub from the ground up

What We’re Looking For

Core Experience

• Bachelor’s or Master’s degree in Computer Science, AI, Machine Learning, Mathematics, Statistics, or a related field
• 2-5 years of experience building and deploying machine learning or AI systems, ideally in fintech, SaaS, or data-intensive domains
• Strong programming skills in Python
• Hands-on experience with ML frameworks such as PyTorch, TensorFlow, or JAX
• Experience with LLMs, prompt engineering, RAG pipelines, or fine-tuning
• Familiarity with cloud platforms (AWS, GCP, or Azure) and containerization (Docker, Kubernetes)
• Experience working with vector databases and embedding-based retrieval systems
• Understanding of software engineering fundamentals: version control, testing, CI/CD

Other

• Strong analytical mindset with the ability to connect model output to real financial/business outcomes
• Excellent communication skills — able to explain model behavior and limitations to non-technical stakeholders (finance/treasury teams)

Preferred Qualifications

• Prior exposure to treasury, banking, or financial-data systems (TMS, ERPs, payment rails)
• Experience with MLOps tools (MLflow, Kubeflow, Weights & Biases)
• Experience building agentic systems or tool-using AI applications
• Familiarity with time-series forecasting techniques and financial anomaly detection

Are you interested in this position?
Apply by clicking on the “Apply Now” button below!
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