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TD Newton
About the role
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Proposed designation: M
anager - ML LLM Engineer
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Role type:
Lead and Individual contributor
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Reporting to :
AI Technology Architect / Head of KDN AI Labs
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Geo to be supported:
KPMG Delivery Network (KDN)
Roles & responsibilities
KPMG Delivery Network (KDN) is seeking a highly skilled ML and LLM engineer to join the team. In the role of Manager of Machine Learning (ML) and Large Language Models (LLM) Engineering, you will serve as a high impact individual contributor and a technical leader. You will bridge the gap between advanced research and production grade engineering, overseeing the lifecycle of LLMs and traditional ML systems. You will be responsible for the architectural integrity of our AI platforms while mentoring senior associates and driving the technical roadmap for KDN’s AI initiatives.
Key responsibilities include:
1.
Architectural Leadership::
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Lead the design and implementation of complete ML and LLM architectures
on Azure, AWS and GCP, ensuring solutions are scalable, secure, and
production ready.
2.
Advanced LLM Engineering:
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Act as a primary individual contributor in
fine tuning LLMs, implementing advanced RAG (Retrieval Augmented
Generation) pipelines, and optimizing model inference using AI Fabric.
3.
Hybrid ML Systems:
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Design systems that integrate traditional ML with Generative AI to solve
complex predictive and generative business problems.
4.
MLOps and Governance:
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Establish rigorous MLOps practices, including automated CICD pipelines for
models, version control for experiments, and monitoring frameworks for
model drift and LLM hallucinations.
5.
Data Strategy:
Oversee the integration of huge volume of datasets from data lakes and databases, ensuring optimal data engineering and feature store management for model training
6. Performance Tuning and Optimization:
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Continuously monitor and optimize AI models and data pipelines to improve
performance, accuracy, and scalability.
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Implement strategies for model fine tuning, hyperparameter optimization,
and feature engineering to enhance AI solution effectiveness.
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Troubleshoot and resolve technical issues related to AI model deployment,
data processing, and cloud infrastructure.
7.
Innovation and Continuous Improvement:
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Stay updated with the latest advancements in Gen AI, cloud computing, and
big data technologies, applying new techniques to improve solutions.
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Experiment with emerging technologies and frameworks to drive innovation.
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Contribute to the development of AI best practices, coding standards, and
technical documentation to ensure consistency and quality across projects.
8. Strategic Execution:
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Work with leadership to translate business requirements into technical
specifications, managing project timelines while remaining hands-on to
solve the most complex technical bottlenecks.
Educational qualifications
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Master’s or PhD in Computer Science, Machine Learning, or a highly
quantitative field.
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Advanced and relevant professional certifications in AI ML, cloud
computing and data engineering are advantageous.
Work experience
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9/10+ years of experience in AI ML, with at least 4 years focused on LLM
implementation and productionalization. Proven track record of moving
models from research and notebooks to large scale production
environments.
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Proven experience with AI frameworks (e.g., TensorFlow, PyTorch), and
experience in working with Databricks, AI Fabric, and Snowflake. Deep
experience in specialized LLM libraries (e.g., Hugging Face, LangChain,
Llama Index).
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Extensive experience with Azure cloud services, including AI and data
services, and a strong background in cloud-native development. Expertise
in coding with Python, TypeScript, Scala, or similar languages, with a
focus on AI ML libraries and big data processing.
Skills
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Ability to lead by example, maintaining high code quality and engineering
standards.
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Proficiency in designing and coding AI models, data pipelines, and
cloud-based solutions. Strong understanding of AI ML algorithms, data
engineering, and model deployment strategies.
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Experience with cloud infrastructure management, particularly in Azure,
and the ability to optimize AI workloads for performance and cost.
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Excellent problem-solving skills and the ability to work collaboratively
in a cross functional team environment and lead by example. Strong
communication skills, with the ability to articulate complex technical
concepts to stakeholders.