AI/ML Ops Engineer (Specializing In LLMs) // LLM Operations Engineer

Mandatory Skills & Experience

• Expertise in designing and optimizing machine-learning operations, with a preference for LLM Ops.
• 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, working 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, and 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 LLM Ops 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
  

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): 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.
• Automate ML tasks across the model lifecycle.
• Contribute to HCL 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.)

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• 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 HCL Sales and Delivery teams.
• Support sales pursuits and enable HCL 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 HCLs position as an industry leader.
• Actively contribute to HCL 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.
  

Skills: observability solutions,rag frameworks,serverless,llm ops or related areas,python,devops,transformer models,verbal and written communication,data science,cncf open source community,automated testing,containers,ansible,kubernetes,generative ai,agile,ci/cd pipelines,microservices-based applications,github/devops lifecycle,scrum,data engineering,version control,scripting/programming,ci/cd open source,ml infrastructure,machine learning operations,object detection,container-based workloads,natural language processing (nlp),segmentation,microservices,technical degree,containerization techniques,machine learning,cloud platforms (azure/aws/gcp),linux/unix shell scripting,sql,deployment processes,llm ops,generative models,terraform,monitoring systems,model optimizations