Lead Data Scientist/AI Engineer
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Key skills for this role
About the Role
Design and implement scalable AI solutions, develop robust data pipelines, and ensure system reliability while applying software engineering best practices.
Key Skills for This Role
Full Job Posting
• Use Case Framing & Solution Design
- Translate client business problems into end-to-end system architectures that combine Data, ML, and software components.
- Lead the design of scalable, modular AI solutions, defining services, interfaces, and data flows.
- Make explicit trade-offs across performance, cost, latency, and maintainability.
- Define success metrics, SLAs, and non-functional requirements (reliability, security, scalability).
• Data Engineering & Feature Systems
- Design and implement robust data pipelines (batch and streaming) with strong guarantees on quality, lineage, and observability.
- Build and manage feature pipelines and feature stores, ensuring consistency between training and inference.
- Collaborate with platform teams to define data models, schemas, and storage strategies.
- Enforce standards for data validation, testing, and monitoring within production systems.
• Applied ML & Production-Grade Development
- Develop ML solutions using production-quality code (Python/JS), following software engineering best practices.
- Structure codebases into maintainable, testable modules, with clear separation of concerns.
- Implement unit, integration, and end-to-end tests for data and ML components.
- Package models and logic into deployable services (APIs, microservices, batch jobs) using modern frameworks.
- Balance model sophistication with system performance, latency, and operational constraints.
• MLOps, DevOps & Platform Integration
- Build and maintain CI/CD pipelines for ML systems, including automated testing, validation, and deployment.
- Containerize and deploy services using Docker, Kubernetes, and cloud-native tooling.
- Implement model versioning, experiment tracking, and artifact management.
- Design monitoring and observability systems (logs, metrics, alerts) for both data and model performance.
- Automate retraining, rollback, and release strategies to ensure system resilience.
• System Reliability, Scalability & Security
- Design systems for high availability, fault tolerance, and horizontal scalability.
- Optimize performance across data pipelines and inference services (latency, throughput, cost).
- Apply secure coding practices, access controls, and data protection standards.
- Manage technical debt and ensure long-term maintainability of production systems.
• Documentation, Standards & Engineering Excellence
- Produce developer-focused documentation (APIs, architecture diagrams, runbooks).
- Establish and enforce coding standards, review processes, and engineering best practices.
- Build reusable libraries, SDKs, and internal frameworks to accelerate delivery.
- Drive continuous improvement in engineering maturity, tooling, and delivery practices across the consultancy.
Technical Expertise
- Coding & data querying: Python (pandas/NumPy) and SQL; writing clean, testable code with Git.
- Statistics & experimentation: Probability/statistics, hypothesis testing, regression, and A/B testing/experimental design.
- ML modelling: Feature engineering, model selection, cross-validation, metrics, and hyperparameter tuning (supervised/unsupervised).
- Data prep & analysis: ETL/EDA, data cleaning, handling missing/outliers, and building insight narratives with visuals.
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