Job
Description
At PwC, our people in data and analytics engineering focus on leveraging advanced technologies and techniques to design and develop robust data solutions for clients. They play a crucial role in transforming raw data into actionable insights, enabling informed decision-making and driving business growth. Those in data science and machine learning engineering at PwC will focus on leveraging advanced analytics and machine learning techniques to extract insights from large datasets and drive data-driven decision making. You will work on developing predictive models, conducting statistical analysis, and creating data visualisations to solve complex business problems. Focused on relationships, you are building meaningful client connections, and learning how to manage and inspire others. Navigating increasingly complex situations, you are growing your personal brand, deepening technical expertise and awareness of your strengths. You are expected to anticipate the needs of your teams and clients, and to deliver quality. Embracing increased ambiguity, you are comfortable when the path forward isn’t clear, you ask questions, and you use these moments as opportunities to grow. Skills Examples of the skills, knowledge, and experiences you need to lead and deliver value at this level include but are not limited to: Respond effectively to the diverse perspectives, needs, and feelings of others. Use a broad range of tools, methodologies and techniques to generate new ideas and solve problems. Use critical thinking to break down complex concepts. Understand the broader objectives of your project or role and how your work fits into the overall strategy. Develop a deeper understanding of the business context and how it is changing. Use reflection to develop self awareness, enhance strengths and address development areas. Interpret data to inform insights and recommendations. Uphold and reinforce professional and technical standards (e.g. refer to specific PwC tax and audit guidance), the Firm's code of conduct, and independence requirements. Role Overview We are seeking a Senior Associate – AI Engineer / MLOps / LLMOps with a passion for building resilient, cloud-native AI systems. In this role, you’ll collaborate with data scientists, researchers, and product teams to build infrastructure, automate pipelines, and deploy models that power intelligent applications at scale. If you enjoy solving real-world engineering challenges at the convergence of AI and software systems, this role is for you. Key Responsibilities Architect and implement AI/ML/GenAI pipelines, automating end-to-end workflows from data ingestion to model deployment and monitoring. Develop scalable, production-grade APIs and services using FastAPI, Flask, or similar frameworks for AI/LLM model inference. Design and maintain containerized AI applications using Docker and Kubernetes. Operationalize Large Language Models (LLMs) and other GenAI models via cloud-native deployment (e.g., Azure ML, AWS Sagemaker, GCP Vertex AI). Manage and monitor model performance post-deployment, applying concepts of MLOps and LLMOps including model versioning, A/B testing, and drift detection. Build and maintain CI/CD pipelines for rapid and secure deployment of AI solutions using tools such as GitHub Actions, Azure DevOps, GitLab CI. Implement security, governance, and compliance standards in AI pipelines. Optimize model serving infrastructure for speed, scalability, and cost-efficiency. Collaborate with AI researchers to translate prototypes into robust production-ready solutions. Required Skills & Experience 4 to 9 years of hands-on experience in AI/ML engineering, MLOps, or DevOps for data science products. Bachelor's degree in Computer Science, Engineering, or related technical field (BE/BTech/MCA). Strong software engineering foundation with hands-on experience in Python, Shell scripting, and familiarity with ML libraries (scikit-learn, transformers, etc.). Experience deploying and maintaining LLM-based applications, including prompt orchestration, fine-tuned models, and agentic workflows. Deep understanding of containerization and orchestration (Docker, Kubernetes, Helm). Experience with CI/CD pipelines, infrastructure-as-code tools (Terraform, CloudFormation), and automated deployment practices. Proficiency in cloud platforms: Azure (preferred), AWS, or GCP – including AI/ML services (e.g., Azure ML, AWS Sagemaker, GCP Vertex AI). Experience managing and monitoring ML lifecycle (training, validation, deployment, feedback loops). Solid understanding of APIs, microservices, and event-driven architecture. Experience with model monitoring/orchestration tools (e.g, Kubeflow, MLflow). Exposure to LLMOps-specific orchestration tools such as LangChain, LangGraph, Haystack, or PromptLayer. Experience with serverless deployments (AWS Lambda, Azure Functions) and GPU-enabled compute instances. Knowledge of data pipelines using tools like Apache Airflow, Prefect, or Azure Data Factory. Exposure to logging and observability tools like ELK stack, Azure Monitor, or Datadog. Good to Have Experience implementing multi-model architecture, serving GenAI models alongside traditional ML models. Knowledge of data versioning tools like DVC, Delta Lake, or LakeFS. Familiarity with distributed systems and optimizing inference pipelines for throughput and latency. Experience with infrastructure cost monitoring and optimization strategies for large-scale AI workloads. It would be great if the candidate has exposure to full-stack ML/DL. Soft Skills & Team Expectations Strong communication and documentation skills; ability to clearly articulate technical concepts to both technical and non-technical audiences. Demonstrated ability to work independently as well as collaboratively in a fast-paced environment. A builder's mindset with a strong desire to innovate, automate, and scale. Comfortable in an agile, iterative development environment. Willingness to mentor junior engineers and contribute to team knowledge growth. Proactive in identifying tech stack improvements, security enhancements, and performance bottlenecks. Show more Show less
