About this role
Line of Service
AdvisoryIndustry/Sector
Not ApplicableSpecialism
Emerging TechnologiesManagement Level
Senior AssociateJob Description & Summary
At PwC, our people in software and product innovation focus on developing cutting-edge software solutions and driving product innovation to meet the evolving needs of clients. These individuals combine technical experience with creative thinking to deliver innovative software products and solutions.In emerging technology at PwC, you will focus on exploring and implementing cutting-edge technologies to drive innovation and transformation for clients. You will work in areas such as artificial intelligence, blockchain, and the internet of things (IoT).
Job Description & Summary: A career in our New Technologies practice, within Application and Emerging Technology services, offers the opportunity to design and build modern, cloud-native solutions on Microsoft Azure that power the next generation of digital businesses. You will help clients create scalable, secure, and high-performing data and AI platforms by leveraging Azure services such as Azure Synapse, Azure Databricks, Azure Data Factory, Azure OpenAI Service, and Azure Cognitive Services. Our team focuses on building end-to-end data pipelines, lakehouse architectures, and enterprise-grade GenAI solutions—including intelligent applications, copilots, and domain-specific assistants—that enable advanced analytics, automation, and innovation. We emphasize secure, compliant, and scalable architectures that help organizations unlock the value of their data and transform how they operate and compete.
Job Description & Summary:
4+ years experience with programming languages such as Python and open-source technologies (PySpark, PyTorch, TensorFlow, LangChain, Transformers)
4+ years of experience with Azure data services like (Azure Synapse Analytics, Databricks, SQL Databases, Azure Machine Learning, Azure Cognitive Services etc.)
The MLOps/LLMOps Engineer will be responsible for designing, operationalizing, and scaling AI/ML and Generative AI systems across cloud environments. The role demands deep expertise in model lifecycle management, enterprise-grade AI architectures, and hands-on implementation of robust, secure, and scalable ML/LLM pipelines. The engineer will collaborate closely with data scientists, cloud engineers, and business stakeholders to ensure seamless development-to-deployment workflows.
Responsibilities:
• Creation and implementation of enterprise grade architectures for deployments of On-premises ML/LLM models for scalability and efficiency
• Working with SEO team for SEO pages generation using LLMs with reduction in AI scores and humanizing the AI generated content
• Fine-tuning LLM models and implementation of RAGs
• Interacting with multiple teams and understanding the new requirements, doing PoC on newly proposed requirements proposing various solutions for optimization, efficiency and cost saving
• Creation of Web Pages (Blogs) using regularly updated LLMs, with using NLP techniques for reduction of AI scores and improvement of SEO rankings
• Implementation of Quality assurance checks for the content being generated from LLMs
• Implementation of LangChains for achieving requirement specific use-cases
• Setting up automated ML pipelines on Azure ML Studio for scheduled training of ML models, with CI/CD setups for automating deployments of trained ML model pickle/joblib dumps on corresponding infra like K8s, Linux Servers using tools like Jenkins and version control tools like Git
• Experience on working with Frontend teams for image optimizations for improving the time and efficiency for rendering the web pages
• Writing shell scripting for monitoring the efficiency of the generated outputs of LLM models and providing corresponding estimates of required resources for scalability requirements
Mandatory skill sets:
Python
Azure & AWS AI/ML Services
LLM Finetuning, RAG Implementation
Enterprise AI/ML Architecture (Cloud + OnPrem)
Docker, Shell Scripting, Kubernetes (AKS, OnPrem K8s)
Google Analytics, Azure Synapse, BigQuery
Jupyter, VSCode, PyCharm
CI/CD ( Git, GitHub/GitLab Pipelines)
Preferred skill sets:
LangChain, NLP Techniques
Automated ML Pipelines, Scheduled Training Jobs
Model Packaging & Deployment (Pickle/Joblib → K8s/Linux Servers)
Frontend Collaboration (Image Optimization, Page Performance)
Monitoring & Resource Estimation for LLM Workloads
Enterprise Architecture for OnPrem LLM Serving
Years of experience required:
4 to 8 years
Education qualification:
BE, Btech / Mtech/ MCA
Education (if blank, degree and/or field of study not specified)
Degrees/Field of Study required: Bachelor of Technology, Master of Business AdministrationDegrees/Field of Study preferred:Certifications (if blank, certifications not specified)
Required Skills
Microsoft AzureOptional Skills
Accepting Feedback, Accepting Feedback, Active Listening, Analytical Thinking, Artificial Intelligence, Business Planning and Simulation (BW-BPS), Communication, Competitive Advantage, Conducting Research, Creativity, Digital Transformation, Embracing Change, Emotional Regulation, Empathy, Implementing Technology, Inclusion, Innovation Processes, Intellectual Curiosity, Internet of Things (IoT), Learning Agility, Optimism, Product Development, Product Testing, Prototyping, Quality Assurance Process Management {+ 10 more}Desired Languages (If blank, desired languages not specified)
Travel Requirements
Available for Work Visa Sponsorship?
Government Clearance Required?
Job Posting End Date
Other facts
About PwC
At PwC, we help clients drive their companies to the leading edge. We’re a tech-forward, people-empowered network with more than 370,000 people in 149 countries. Across audit and assurance, tax and legal, deals and consulting we help build, accelerate and sustain momentum. Find out more at www.pwc.com.
PwC: Audit and assurance, consulting and tax services
PwC refers to the PwC network and/or one or more of its member firms, each of which is a separate legal entity. Content on this page has been prepared for general information only and is not intended to be relied upon as accounting, tax or professional advice. Please reach out to your advisors for specific advice.
What you'll do
- The MLOps/LLMOps Engineer will be responsible for designing, operationalizing, and scaling AI/ML and Generative AI systems across cloud environments, focusing on model lifecycle management and implementing robust, secure, and scalable ML/LLM pipelines. Responsibilities include creating enterprise-grade architectures for on-premises LLM deployments, fine-tuning models, implementing RAGs, and setting up automated ML pipelines on Azure ML Studio with CI/CD.
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