LLM Ops Engineer

Description & Requirements
Position Summary
LLMOps(Large language model operations) Engineer will play a pivotal role in building and maintaining the infrastructure and pipelines for our cutting-edge Generative AI applications, establishing efficient and scalable systems for LLM research, evaluation, training, and fine-tuning. Engineer will be responsible for managing and optimizing large language models (LLMs) across various platforms This position is uniquely tailored for those who excel in crafting pipelines, cloud infrastructure, environments, and workflows. Your expertise in automating and streamlining the ML lifecycle will be instrumental in ensuring the efficiency, scalability, and reliability of our Generative AI models and associated platform. LLMOps engineers expertise will ensure the smooth deployment, maintenance, and performance of these AI platforms and powerful large language models.
You will follow Site Reliability Engineering & MLOps principles and will be encouraged to contribute your own best practices and ideas to our ways of working.
Reporting to the Head of Cloud Native operations, you will be an experienced thought leader, and comfortable engaging senior managers and technologists. You will engage with clients, display technical leadership, and guide the creation of efficient and complex products/solutions.

Key Responsibilities

Technical & Architectural Leadership

Contribute to the technical delivery of projects, ensuring a high quality of work that adheres to best practices, brings innovative approaches and meets client expectations. Project types include following (but not limited to):
o Solution architecture, Proof of concepts (PoCs), MVP, design, develop, and implementation of ML/LLM pipelines for generative AI models, encompassing data ingestion, pre-processing, training, deployment, and monitoring.
o Automate ML tasks across the model lifecycle.
Contribute to thought leadership across the Cloud Native domain with an expert understanding of advanced AI solutions using Large Language Models (LLM) & Natural Language Processing (NLP) techniques and partner technologies.
Collaborate with cross-functional teams to integrate LLM and NLP technologies into existing systems.
Ensure the highest levels of security and compliance are maintained in all ML and LLM operations.
Stay abreast of the latest developments in ML and LLM technologies and methodologies, integrating these innovations to enhance operational efficiency and model effectiveness.
Collaborate with global peers from partner ecosystems on joint technical projects. This partner ecosystem includes Google, Microsoft, AWS, IBM, Red Hat, Intel, Cisco, and Dell / VMware etc.

Service Delivery

Provide a technical hands-on contribution. Create scalable infra to support enterprise loads (distributed GPU compute, foundation models, orchestrating across multiple cloud vendors, etc.)
Ensuring the reliable and efficient platform operations.
Apply data science, machine learning, deep learning, and natural language processing methods to analyse, process, and improve the models data and performance.
Create and optimize prompts and queries for retrieval augmented generation and prompt engineering techniques to enhance the models capabilities and user experience w.r.t Operations & associated platforms.
Client-facing influence and guidance, engaging in consultative client discussions and performing a Trusted Advisor role.
Provide effective support to Sales and Delivery teams.
Support sales pursuits and enable revenue growth.
Define the modernization strategy for client platform and associated IT practices, create solution architecture and provide oversight of the client journey.

Innovation & Initiative

Always maintain hands-on technical credibility, keep in front of the industry, and be prepared to show and lead the way forward to others.
Engage in technical innovation and support position as an industry leader.
Actively contribute to sponsorship of leading industry bodies such as the CNCF and Linux Foundation.
Contribute to thought leadership by writing Whitepapers, blogs, and speaking at industry events.
Be a trusted, knowledgeable internal innovator driving success across our global workforce.

Client Relationships

Advise on best practices related to platform & Operations engineering and cloud native operations, run client briefings and workshops, and engage technical leaders in a strategic dialogue.
Develop and maintain strong relationships with client stakeholders.
Perform a Trusted Advisor role.
Contribute to technical projects with a strong focus on technical excellence and on-time delivery.

Mandatory Skills & Experience

Expertise in designing and optimizing machine-learning operations, with a preference for LLMOps.
Proficient in Data Science, Machine Learning, Python, SQL, Linux/Unix shell scripting.
Experience on Large Language Models and Natural Language Processing (NLP), and experience with researching, training, and fine-tuning LLMs. Contribute towards fine-tune Transformer models for optimal performance in NLP tasks, if required.
Implement and maintain automated testing and deployment processes for machine learning models w.r.t LLMOps.
Implement version control, CI/CD pipelines, and containerization techniques to streamline ML and LLM workflows.

Develop and maintain robust monitoring and alerting systems for generative AI models ensuring proactive identification and resolution of issues.
Research or engineering experience in deep learning with one or more of the following: generative models, segmentation, object detection, classification, model optimisations.
Experience implementing RAG frameworks as part of available-ready products.
Experience in setting up the infrastructure for the latest technology such as Kubernetes, Serverless, Containers, Microservices etc.
Experience in scripting / programming to automate deployments and testing, worked on tools like Terraform and Ansible. Scripting languages like Python, bash, YAML etc.
Experience on CI/CD opensource and enterprise tool sets such as Argo CD, Jenkins (others like Jenkins X, Circle CI, Argo CD, Tekton, Travis, Concourse an advantage).
Experience with the GitHub/DevOps Lifecycle
Experience in Observability solutions (Prometheus, EFK stacks, ELK stacks, Grafana, Dynatrace, AppDynamics)
Experience in at-least one of the clouds for example - Azure/AWS/GCP
Significant experience on microservices-based, container-based or similar modern approaches of applications and workloads.
You have exemplary verbal and written communication skills (English). Able to interact and influence at the highest level, you will be a confident presenter and speaker, able to command the respect of your audience.

Desired Skills & Experience
Bachelor level technical degree or equivalent experience; Computer Science, Data Science, or
Engineering background preferred; Masters Degree desired.
Experience in LLMOps or related areas, such as DevOps, data engineering, or ML infrastructure.
Hands-on experience in deploying and managing machine learning and large language model pipelines in cloud platforms (e.g., AWS, Azure) for ML workloads.
Familiar with data science, machine learning, deep learning, and natural language processing concepts, tools, and libraries such as Python, TensorFlow, PyTorch, NLTK etc.
Experience in using retrieval augmented generation and prompt engineering techniques to improve the models quality and diversity to improve operations efficiency. Proven experience in developing and fine-tuning Language Models (LLMs).
Stay up-to-date with the latest advancements in Generative AI, conduct research, and explore innovative techniques to improve model quality and efficiency.
The perfect candidate will already be working within a System Integrator, Consulting or Enterprise organisation with 8+ years of experience in a technical role within the Cloud domain.
Deep understanding of core practices including SRE, Agile, Scrum, XP and Domain Driven Design. Familiarity with the CNCF open-source community.
Enjoy working in a fast-paced and dynamic environment using the latest technologies