- Company Name
- Crisil
- Job Title
- AI MLOps Lead - Payments Engineering
- Job Description
-
**Job Title:** AI MLOps Lead – Payments Engineering
**Role Summary**
Lead the design, development, and deployment of AI‑powered payment solutions across a full payments stack (SWIFT, ISO 20022, ACH, cards, cross‑border, liquidity). Act as a techno‑functional bridge between business stakeholders and engineering teams, delivering end‑to‑end AI solutions that address fraud, reconciliation, routing, and regulatory compliance.
**Expectations**
- 10+ years in banking/fintech with deep payments domain expertise.
- Proven record of architecting and delivering production AI/ML pipelines in a regulated environment.
- Strong Python programming, cloud and MLOps proficiency, and API development skills.
- Ability to translate complex business requirements into scalable, compliant AI solutions.
**Key Responsibilities**
1. **Payments Domain & Functional Leadership** – Engage stakeholders, identify AI opportunities, translate regulatory challenges into solution use cases, and define business requirements.
2. **AI Solution Design** – Architect AI/ML approaches (ML, LLM, hybrid) for fraud detection, reconciliation, routing, exception handling, and AML pattern detection; design cloud‑native scalable systems.
3. **Hands‑on Development** – Build models and pipelines in Python, prepare data, engineer features, evaluate models, and expose APIs for integration.
4. **AI MLOps & Deployment** – Implement MLOps workflows (model versioning, CI/CD, monitoring, drift detection, governance) using tools such as MLflow, Kubeflow, SageMaker.
5. **Stakeholder & Delivery Management** – Lead POCs, scale into production, collaborate with product, risk, operations, and engineering teams, and contribute to client proposals and solution articulations.
**Required Skills**
- Python, ML libraries (Scikit‑learn, TensorFlow, PyTorch, XGBoost).
- LLM/GenAI frameworks (LangChain, RAG, prompt engineering).
- Cloud platforms (AWS, Azure, GCP).
- MLOps tools (MLflow, Kubeflow, SageMaker, Azure ML).
- API development and data pipeline engineering (real‑time & batch).
- Strong solution architecture and business‑to‑tech translation.
- Knowledge of payments regulations, AML, and fraud detection.
**Required Education & Certifications**
- Bachelor’s or Master’s in Computer Science, Engineering, Finance, or related field.
- Certifications in AWS/Azure/GCP (e.g., AWS Solutions Architect, Azure AI Engineer) and MLOps frameworks are preferred.