MLOps Engineer

Role Overview

We are looking for a Senior AI/ML & MLOps Engineer with 3–6 years of experience to build, deploy, and scale AI/ML systems in production. The role will combine AI/ML engineering, MLOps, backend/API development, cloud infrastructure, and AI platform optimization.

The person will work closely with AI, Product, Credit, Engineering, DevOps and Data teams to take AI initiatives from experimentation to reliable production systems.

Key Responsibilities

• Design, develop, and deploy ML/AI models and AI-powered applications for production use cases.
• Build and maintain MLOps pipelines for model training, evaluation, deployment, monitoring, and retraining.
• Develop scalable APIs and backend services to integrate AI/ML models with business applications.
• Own end-to-end productionization of AI solutions, including architecture, deployment, monitoring, and reliability.
• Work on AWS/cloud infrastructure for AI and data workloads and optimize infrastructure for performance and cost.
• Build frameworks and processes to monitor and control AI/LLM/API costs.
• Implement model and system monitoring, including performance, drift, latency, failures, data quality, and business KPIs.
• Work with data engineers to build reliable data pipelines and data platforms required for AI/ML systems.
• Evaluate and integrate LLMs, GenAI models, AI agents, and third-party AI services where applicable.
• Build experimentation frameworks to quickly evaluate new AI models, tools, and approaches.
• Ensure AI systems are scalable, secure, observable and production-ready.
• Troubleshoot production issues and work with DevOps/Engineering teams for quick resolution.
• Maintain technical documentation, architecture decisions, deployment processes, and operational runbooks.
• Drive engineering best practices around Git, CI/CD, Docker, cloud deployment, testing, and code quality.
• Mentor junior engineers and contribute to technical design and architecture discussions.

Required Skills

• 3–5 years of experience in AI/ML Engineering, MLOps.
• Strong Python programming skills.
• Hands-on experience with ML model development and deployment.
• Strong understanding of MLOps and ML lifecycle management.
• Experience building and deploying REST APIs / microservices.
• Experience with AWS or other cloud platforms.
• Strong knowledge of Docker, Git, CI/CD, databases, and production deployment.
• Experience with ML frameworks such as Scikit-learn, XGBoost, PyTorch, or TensorFlow.
• Experience with model monitoring, data pipelines, logging, alerting, and observability.
• Working knowledge of LLMs, GenAI, prompt engineering, RAG, or AI agents is preferred.
• Good understanding of system design, scalability, performance and cost optimization.

Good to Have

• Experience in FinTech, lending, credit, risk, or financial services.
• Experience with Vertex AI, AWS ML services or equivalent ML platforms.
• Experience building AI/ML platforms or reusable frameworks.
• Experience with PostgreSQL and data engineering.
• Experience optimizing cloud and AI infrastructure costs.
• Experience working with autonomous/agentic AI systems.

What We Expect

• Strong ownership from problem definition to production deployment.
• Ability to work independently and handle ambiguous AI/ML problems.
• Strong collaboration with Product, Engineering, DevOps, Data, and Business teams.
• Focus on building reliable, scalable, and cost-efficient AI systems, not just models or prototypes.