AI Lead / Principal AI Engineer
Role Summary
The AI Engineer is responsible for leading the design, development, deployment, and operationalization of scalable AI/ML solutions in production environments. The role bridges client requirements real-world enterprise use cases and data science models ready for deployment, ensuring models are robust, optimised, compliant, and continuously improved. Also plays a role of an AI Lead to provide technical leadership, mentors engineers, and drives end‑to‑end ownership of AI initiatives and drive them to completion as per the Business standards.
Key Responsibilities
1.AI/ML Engineering & Productionization
- Lead the development and deployment of scalable, optimized AI/ML solutions into production environments.
- Translate theoretical Business use cases into enterprise‑grade AI solutions with performance, security, and reliability considerations.
2.Data Engineering & Pipeline Design
- Architect and build algorithms and pipelines for the extraction, transformation, and loading (ETL/ELT) of large volumes of structured and unstructured data, including near‑real‑time data feeds.
- Work with diverse data sources such as documents, APIs, logs, databases, and enterprise knowledge repositories.
- Ensure data quality, latency optimization, and scalability of pipelines supporting AI workloads.
3.Model Evaluation, Experimentation & Optimization
- Monitor deployed models for drift, degradation, and failures, and implement corrective actions.
- Identify, troubleshoot, and resolve model, data, and pipeline bugs arising in production environments.
- Run experiments to test the performance of deployed models, and identifies and resolves bugs that arise in the process.
4.Platform & Deployment Ownership
- Work with AI/ML platforms and software ecosystems SPOCs where models are deployed (cloud, internal platforms, APIs, MLOps tools).
- Review deployment architectures, API integrations, access controls, and operational run‑books.
- Support UAT, production releases, and ongoing production stability.
5.Technical Leadership & Collaboration
- Lead and mentor AI engineers, ensuring adherence to best practices in coding, testing, and MLOps.
- Collaborate closely with product owners, platform teams, security, and stakeholders to deliver end‑to‑end AI solutions.
- Apply strong foundations in statistics, programming, and scripting to guide solution design and reviews.
Required Skills & Competencies
- Strong experience in AI/ML engineering.
- Solid foundation in statistics, machine learning, and data engineering.
- Proficiency in relevant programming and scripting languages (e.g., Python, SQL others as required by the firm).
- Experience working with AI/ML deployment platforms, APIs, and production environments.
- Ability to drive solutions in a team‑based, enterprise delivery setting with high ownership.
