AI Engineer

AI Engineer

Full-Time 43200 - 72000 £ / year (est.) Home office (partial)
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At a Glance

  • Tasks: Lead AI/ML Ops for Next Best Offer models, ensuring smooth operations and enhancements.
  • Company: Join HCLTech, a global tech leader with over 219,000 employees across 54 countries.
  • Benefits: Enjoy competitive pay, remote work options, and opportunities for professional growth.
  • Why this job: Be at the forefront of AI innovation, impacting various industries while collaborating with talented teams.
  • Qualifications: 10+ years in Data Science/ML, strong Python skills, and experience with recommendation systems required.
  • Other info: Mentorship opportunities available; ideal for those passionate about cutting-edge technology.

The predicted salary is between 43200 - 72000 £ per year.

HCLTech is a global technology company, home to 219,000+ people across 54 countries, delivering industry-leading capabilities centered on digital, engineering and cloud, 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 $13+ billion.

Increase your chances of an interview by reading the following overview of this role before making an application.

Job Description 2: Lead AI/ML Ops Engineer (Recommendation Systems Focus)

  • Role: Lead AI/ML Ops Engineer
  • Experience Level: Senior
  • Core Objective: Take a leading role in maintaining the operational stability, performance, and ongoing development of the Next Best Offer (NBO) models. Oversee necessary enhancements, ensure smooth execution across various NBO modules (Alert, Core, Eval, Scoring, Retention), and facilitate effective knowledge transfer.
  • Key Responsibilities:
  • Oversee the day-to-day operations, monitoring, and troubleshooting of the NBO models running on GCP and potentially on-premise servers.
  • Lead the design, implementation, and validation of new business requirements and enhancements for the NBO suite.
  • Ensure continuous monitoring, supervision, and version upgrades of the diverse NBO models.
  • Guide the further development of ML models within NBO, potentially incorporating advanced techniques like GNNs, autoencoders, or transformers.
  • Oversee data validation processes and ensure data quality for NBO inputs.
  • Manage the NBO model evaluation framework (NBO Eval) and report on performance metrics (accuracy, coverage, personalization).
  • Coordinate with relevant departments regarding architecture maintenance and regulatory aspects if applicable to NBO.
  • Collaborate closely with stakeholders to communicate model performance, development progress, and quarterly benefits where applicable.
  • Mentor mid-level engineers within the vendor team, particularly on NBO specifics.
  • Actively participate in and help coordinate the intensive knowledge handover from departing employees within the first 1.5-2 months.
  • Lead the documentation efforts for NBO processes, models, and enhancements.
  • Support the final handover process to the new internal team towards the end of the engagement.
  • Required Skills & Experience:
  • 10+ years of hands-on experience in Data Science and Machine Learning, applying models in a business context, particularly for personalization or recommendation.
  • Proven experience leading the development, deployment, and lifecycle management of complex ML systems.
  • Strong programming skills in Python and potentially R.
  • Expertise with relevant ML frameworks (e.g., scikit-learn, PyTorch).
  • Experience with recommendation systems, collaborative filtering, and ideally Graph Neural Networks (GNNs).
  • Proficient with SQL and working with large datasets (e.g., GCP BigQuery).
  • Experience with cloud platforms (specifically GCP) and CI/CD processes (specifically GitHub Actions).
  • Experience deploying models in containerized applications.
  • Excellent problem-solving skills and ability to quickly adapt to new model types and requirements.
  • Strong communication and stakeholder management skills.
  • Expertise in advanced neural network architectures (Autoencoders, Transformers).
  • Ability to rapidly acquire complex domain knowledge.

AI Engineer employer: HCLTech

HCLTech is an exceptional employer for AI Engineers, offering a dynamic work environment that fosters innovation and collaboration across diverse industries. With a strong focus on employee growth, HCLTech provides ample opportunities for professional development and mentorship, ensuring that team members can thrive in their careers while contributing to cutting-edge projects. Located in a global hub of technology, employees benefit from a rich culture of diversity and inclusion, making it a rewarding place to work.
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Contact Detail:

HCLTech Recruiting Team

StudySmarter Expert Advice 🤫

We think this is how you could land AI Engineer

✨Tip Number 1

Familiarise yourself with the specific technologies mentioned in the job description, such as GCP, Python, and relevant ML frameworks like PyTorch. Having hands-on experience or projects that showcase your skills in these areas will make you stand out.

✨Tip Number 2

Network with current or former employees of HCLTech, especially those in AI/ML roles. They can provide valuable insights into the company culture and expectations, which can help you tailor your approach during interviews.

✨Tip Number 3

Prepare to discuss your experience with recommendation systems and any advanced techniques you've used, such as GNNs or autoencoders. Be ready to explain how you've applied these in real-world scenarios to demonstrate your expertise.

✨Tip Number 4

Showcase your leadership skills by preparing examples of how you've mentored others or led projects in the past. This role requires guiding mid-level engineers, so highlighting your ability to lead and communicate effectively will be crucial.

We think you need these skills to ace AI Engineer

Data Science
Machine Learning
Recommendation Systems
Graph Neural Networks (GNNs)
Python Programming
R Programming
ML Frameworks (e.g., scikit-learn, PyTorch)
SQL
GCP BigQuery
Cloud Platforms (specifically GCP)
CI/CD Processes (specifically GitHub Actions)
Containerization
Problem-Solving Skills
Stakeholder Management
Advanced Neural Network Architectures (Autoencoders, Transformers)
Adaptability
Documentation Skills
Mentoring Skills

Some tips for your application 🫡

Understand the Role: Before applying, make sure you thoroughly understand the responsibilities and requirements of the Lead AI/ML Ops Engineer position. Tailor your application to highlight relevant experience in maintaining operational stability and developing recommendation systems.

Highlight Relevant Experience: In your CV and cover letter, emphasise your 10+ years of experience in Data Science and Machine Learning. Be specific about your hands-on work with recommendation systems and any leadership roles you've held in developing and deploying ML models.

Showcase Technical Skills: Clearly outline your programming skills in Python and R, as well as your expertise with ML frameworks like scikit-learn and PyTorch. Mention your experience with cloud platforms, particularly GCP, and any familiarity with CI/CD processes.

Craft a Compelling Cover Letter: Use your cover letter to tell a story about your journey in AI and ML. Discuss specific projects where you've led teams or mentored others, and how your problem-solving skills have contributed to successful outcomes in previous roles.

How to prepare for a job interview at HCLTech

✨Showcase Your Technical Expertise

Be prepared to discuss your hands-on experience with Data Science and Machine Learning. Highlight specific projects where you've applied models in a business context, especially focusing on recommendation systems and any advanced techniques like GNNs or transformers.

✨Demonstrate Problem-Solving Skills

Expect to face technical challenges during the interview. Prepare to explain your approach to troubleshooting and optimising ML models, particularly in relation to operational stability and performance metrics.

✨Communicate Effectively

Strong communication is key for this role. Practice articulating complex concepts clearly and concisely, especially when discussing model performance and development progress with stakeholders.

✨Prepare for Leadership Questions

As a Lead AI/ML Ops Engineer, you'll need to mentor others and coordinate knowledge transfer. Be ready to share examples of how you've led teams, managed projects, and facilitated collaboration in previous roles.

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