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 / B.Tech / MCA / M.Sc / M.E / M.Tech /Master’s Degree /MBA
