MLOps/LLMOps Engineer-Senior Associate
MLOps/LLMOps Engineer-Senior Associate
Posted on :
08-07-2025
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1 Vacancy
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Job Description
Line of Service
AdvisoryIndustry/Sector
Not ApplicableSpecialism
Advisory - OtherManagement Level
Senior AssociateJob Description & Summary
At PwC our people in data and analytics focus on leveraging data to drive insights and make informed business decisions. They utilise advanced analytics techniques to help clients optimise their operations and achieve their strategic goals.In data analysis at PwC you will focus on utilising advanced analytical techniques to extract insights from large datasets and drive data-driven decision-making. You will leverage skills in data manipulation visualisation and statistical modelling to support clients in solving complex business problems.
PwC US - Acceleration Center is seeking a highly skilled MLOps/LLMOps Engineer who will play a critical role in the deployment scaling and maintenance of Generative AI models. This position involves close collaboration with data scientists ML/GenAI engineers and DevOps teams to ensure seamless integration and operation of GenAI models within production environments at PwC as well as our clients. The ideal candidate will have a strong background in MLOps practices along with experience and interest in Generative AI technologies.
Years of Experience: Candidates with 4 years of hands on experience
Core Qualifications:
3 years of hands-on experience developing and deploying AI models in production environments with 1 year of experience in developing proofs of concept and prototypes
Strong background in software development with experience in building and maintaining scalable distributed systems
Strong programming skills in languages like Python and familiarity in ML frameworks and libraries (e.g. TensorFlow PyTorch)
Knowledge of containerization and orchestration tools like Docker and Kubernetes.
Familiarity with cloud platforms (AWS GCP Azure) and their ML/AI service offerings
Experience with continuous integration and delivery tools such as Jenkins GitLab CI/CD or CircleCI.
Experience with infrastructure as code tools like Terraform or CloudFormation.
Technical Skills:
Must to Have:
Proficiency with MLOps tools such as MLflow Kubeflow Airflow or similar for managing machine learning workflows and lifecycle.
Practical understanding of generative AI frameworks (e.g. HuggingFace Transformers OpenAI GPT DALL-E)
Expertise in containerization technologies like Docker and orchestration tools such as Kubernetes for scalable model deployment.
Expertise in MLOps and LLMOps practices including CI/CD for ML models
Strong knowledge of one or more cloud-based AI services (e.g. AWS SageMaker Azure ML Google Vertex AI)
Nice to Have:
Experience with advanced GenAI applications such as natural language generation image synthesis and creative AI.
Familiarity with experiment tracking and model registry tools.
Knowledge of high-performance computing and parallel processing techniques.
Contributions to open-source MLOps or GenAI projects.
Key Responsibilities:
Develop and implement MLOps strategies tailored for Generative AI models to ensure robustness scalability and reliability.
Design and manage CI/CD pipelines specialized for ML workflows including the deployment of generative models such as GANs VAEs and Transformers.
Monitor and optimize the performance of AI models in production employing tools and techniques for continuous validation retraining and A/B testing.
Collaborate with data scientists and ML researchers to understand model requirements and translate them into scalable operational frameworks.
Implement best practices for version control containerization infrastructure automation and orchestration using industry-standard tools (e.g. Docker Kubernetes).
Ensure compliance with data privacy regulations and company policies during model deployment and operation.
Troubleshoot and resolve issues related to ML model serving data anomalies and infrastructure performance.
Stay up-to-date with the latest developments in MLOps and Generative AI bringing innovative solutions to enhance our AI capabilities.
Project Delivery:
Design and implement scalable and reliable deployment pipelines for ML/GenAI models to move them from development to production environments
Ensure models are deployed with appropriate versioning and rollback mechanisms to maintain stability and ease of updates.
Oversee the cloud infrastructure setup automated data ingestion pipelines ensuring they meets the needs of GenAI workloads in terms of computation power storage and network requirements.
Create detailed documentation for deployment pipelines monitoring setups and operational procedures to ensure transparency and ease of maintenance.
Actively participate in retrospectives to identify areas for improvement in the deployment process.
Client Engagement:
Collaborate with clients to understand their business needs goals and specific requirements for Generative AI solutions.
Collaborate with solution architects to design ML/LLMOps that meet client needs
Present technical approaches and results to both technical and non-technical stakeholders
Conduct training sessions and workshops for client teams to help them understand operate and maintain the deployed AI models.
Create comprehensive documentation and user guides to assist clients in managing and leveraging the Generative AI solutions effectively.
Innovation and Knowledge Sharing:
Stay updated with the latest trends research and advancements in MLOps/LLMOps and Generative AI and apply this knowledge to improve existing systems and processes.
Develop internal tools and frameworks to accelerate ML/GenAI model development and deployment
Mentor junior team members on MLOps/LLMOps best practices
Contribute to technical blog posts and whitepapers on MLOps/LLMOps
Professional and Educational Background:
Any graduate /BE / / MCA / / M.E / /Masters Degree /MBA
Education (if blank degree and/or field of study not specified)
Degrees/Field of Study required:Degrees/Field of Study preferred:Certifications (if blank certifications not specified)
Required Skills
Optional Skills
Accepting Feedback Accepting Feedback Active Listening Algorithm Development Alteryx (Automation Platform) Analytical Thinking Analytic Research Big Data Business Data Analytics Communication Complex Data Analysis Conducting Research Creativity Customer Analysis Customer Needs Analysis Dashboard Creation Data Analysis Data Analysis Software Data Collection Data-Driven Insights Data Integration Data Integrity Data Mining Data Modeling Data Pipeline 38 moreDesired Languages (If blank desired languages not specified)
Travel Requirements
Not SpecifiedAvailable for Work Visa Sponsorship
NoGovernment Clearance Required
NoJob Posting End Date
Required Experience:
Senior IC
Employment Type
Full-Time
Key Skills
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