HCLTech is a global technology company, home to more than 224,000 people across 60 countries, delivering industry-leading capabilities centered around digital, engineering, cloud, AI and software, powered by a broad portfolio of technology services and products. We work with clients across all major verticals, providing industry solutions for Financial Services, Manufacturing, Life Sciences and Healthcare, Technology and Services, Telecom and Media, Retail and CPG, and Public Services. Consolidated revenues as of 12 months ending December 2023 totaled $13.1 billion.

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ML Engineer

ML Engineers are the engineer AI/ML systems. They leverage their expertise in software engineering, machine learning, data engineering to design, develop, and deploy AI/ML models into real-world applications.

In this role we value:

Software Engineering Skill

ML Engineers must be skilled in engineering of machine learning systems to handle data pipeline, deploy machine learning models, and monitor their performance. Key tasks include:

• Model deployment: Transitioning models from development into production environments.
• Data pipelines: Building and maintaining data flows to ensure smooth operations.
• Scalability: Ensuring models can scale efficiently to handle growing data.
• Model monitoring: Tracking the performance of models and retraining when necessary to avoid performance degradation.
• Optimization: Streamlining model execution for faster inference and better resource utilization.

Communication

Effective communication with cross-functional within development teams, including data scientists, data engineers, and product managers, is crucial for ML Engineers. It involves:

• Collaborating with data scientists: Understanding model requirements and assisting in transitioning models into production.
• Explaining technical limitations: Clearly communicating the trade-offs between model performance, scalability, and infrastructure.
• Documenting processes: Providing detailed documentation to ensure models can be maintained and updated by others.
• Bridging gaps: Acting as the connection between data science and engineering teams to facilitate smooth integration of models into the production ecosystem.

Curiosity and Problem-Solving

ML Engineers should constantly explore ways to improve system efficiency, model accuracy, and data handling. Curiosity leads to innovative approaches to production challenges. It includes:

• Problem diagnosis: Identifying and addressing bottlenecks in model performance and system efficiency.
• Critical thinking: Analyzing deployment failures or inefficiencies and finding technical solutions.
• System improvements: Innovating ways to improve automation, deployment pipelines, and model lifecycle management.

Key Artefacts:

• ML System Design
• Deployed Models
• Data Pipelines
• Code Repositories
• CI/CD Pipelines
• Model Monitoring System
• Logs & Performance Metrics