Role Overview :

This role requires a combination of advanced data science expertise, strong system design capabilities, and hands-on technical leadership. You will drive architectural decisions, guide teams through complex ML implementation challenges, and partner with business, data engineering, DevOps, cloud, and governance stakeholders to deliver production-grade AI/ML capabilities.

Key Responsibilities :

- Lead the architecture and implementation of end-to-end ML solutions, covering data ingestion, feature engineering, training, validation, deployment, inference, monitoring, and lifecycle management.

- Translate business objectives and technical requirements into scalable ML solution designs, selecting appropriate algorithms, infrastructure, deployment patterns, and operational approaches.

- Develop and optimize advanced ML models for classification, regression, forecasting, anomaly detection, and complex business use cases.

- Design enterprise-grade ML pipeline architectures supporting reusable components, parallel execution, distributed processing, automated orchestration, and high-volume workloads.

- Establish standards for Python application architecture, reusable libraries, API contracts, configuration management, testing, code quality, and production readiness.

- Define and drive MLOps strategy, including experiment tracking, model registries, CI/CD, automated validation, release management, deployment approvals, rollback, and reproducibility.

- Architect batch and real-time inference solutions, considering throughput, latency, availability, scalability, fault tolerance, and infrastructure costs.

- Design comprehensive model observability frameworks covering model performance, data quality, data and model drift, service health, latency, and operational alerts.

- Establish automated retraining and model promotion frameworks with appropriate validation gates, human oversight, and governance controls.

- Drive performance engineering and cost optimization across data processing, model inference, compute resources, and cloud infrastructure.

- Define reference architectures and reusable ML engineering frameworks that accelerate solution delivery and standardize implementation across teams.

- Evaluate and guide the adoption of cloud ML platforms, distributed computing technologies, feature stores, containerization, orchestration, and model serving frameworks.

- Partner with data engineering, application engineering, DevOps, security, and cloud architecture teams to ensure seamless integration with enterprise platforms.

- Lead technical reviews, architecture discussions, code reviews, and root-cause analysis for complex production incidents.

- Mentor data scientists and ML engineers, establish engineering best practices, and guide teams on model development, productionization, and operational excellence.

- Ensure compliance with enterprise security, privacy, model governance, explainability, auditability, and responsible AI requirements.

- Engage with business and technology stakeholders to communicate solution trade-offs, delivery risks, performance outcomes, and technical recommendations.

Required Skills & Expertise :

- Extensive hands-on experience in Python, SQL, ML model development, and production software engineering.

- Proven expertise in architecting and deploying end-to-end ML solutions in production environments.

- Strong understanding of system design, distributed processing, scalable pipeline architectures, APIs, batch processing, and real-time inference.

- Deep experience implementing MLOps frameworks, including experiment tracking, model registries, CI/CD, automated testing, deployment automation, and model lifecycle management.

- Strong proficiency in designing reliable, reusable, configurable, and observable ML pipelines.

- Experience implementing model monitoring, drift detection, automated validation, retraining strategies, and production incident management.

- Demonstrated ability to optimize processing performance, inference latency, resource utilization, and cloud infrastructure costs.

- Experience with cloud ML platforms such as Azure ML, AWS SageMaker, GCP Vertex AI, or Databricks.

- Strong knowledge of Docker, Kubernetes, Spark/PySpark, workflow orchestration, and cloud-native deployment patterns.

- Experience establishing technical standards, reviewing solution architectures, and guiding engineering teams.

- Excellent stakeholder management, communication, problem-solving, and technical decision-making skills.

Preferred Qualifications :

- Experience building enterprise ML platforms, shared ML frameworks, feature stores, or reusable model-serving infrastructure.

- Exposure to streaming architectures, distributed inference, model serving optimization, and high-availability production systems.

- Experience in banking, lending, credit risk, fraud detection, financial crime, or other regulated financial services use cases.

- Strong understanding of model explainability, responsible AI, model risk management, audit trails, and regulatory governance.

- Experience working with cross-functional or geographically distributed teams and influencing technical strategy.

- Bachelor's or master's degree in Computer Science, Data Science, Engineering, Mathematics, Statistics, or a related discipline.