PwC—IN · Manager · Gen AI · Data and Analytics · Advisory · Pune

Job description

Description: Line of Service Advisory Industry/Sector Not Applicable Specialism Data, Analytics & AI Management Level Manager Job 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. Why PWC At PwC, you will be part of a vibrant community of solvers that leads with trust and creates distinctive outcomes for our clients and communities. This purpose-led and values-driven work, powered by technology in an environment that drives innovation, will enable you to make a tangible impact in the real world. We reward your contributions, support your wellbeing, and offer inclusive benefits, flexibility programmes and mentorship that will help you thrive in work and life. Together, we grow, learn, care, collaborate, and create a future of infinite experiences for each other. Learn more about us At PwC, we believe in providing equal employment opportunities, without any discrimination on the grounds of gender, ethnic background, age, disability, marital status, sexual orientation, pregnancy, gender identity or expression, religion or other beliefs, perceived differences and status protected by law. We strive to create an environment where each one of our people can bring their true selves and contribute to their personal growth and the firm’s growth. To enable this, we have zero tolerance for any discrimination and harassment based on the above considerations. " Responsibilities: ML Pipeline Design: · Design ML pipelines for experiment management, model management, feature management, and model retraining. · Design APIs for model inferencing at scale. · Proven expertise with MLflow, SageMaker, Vertex AI, and Azure AI. · LLM Serving and GPU Architecture: · Possess deep knowledge of GPU architectures. · Expertise in distributed training and serving of large language models. · Proficient in model and data parallel training using frameworks like DeepSpeed and service frameworks like vLLM. · Model Fine-Tuning and Optimization: · Demonstrate proven expertise in model fine-tuning and optimization techniques. · Achieve better latencies and accuracies in model results. · Reduce training and resource requirements for fine-tuning LLM and LVM models. · DevOps and LLMOps Proficiency: · Proven expertise in DevOps and LLMOps practices. · Knowledgeable in Kubernetes, Docker, and container orchestration. · Deep understanding of LLM orchestration frameworks like Flowise, Langflow, and Langgraph. · Skill Matrix · LLM: Hugging Face OSS LLMs, GPT, Gemini, Claude, Mixtral, Llama · LLM Ops: ML Flow, Langchain, Langraph, LangFlow, Flowise, LLamaIndex, SageMaker, AWS Bedrock, Vertex AI, Azure AI · Databases/Datawarehouse: DynamoDB, Cosmos, MongoDB, RDS, MySQL, PostGreSQL, Aurora, Spanner, Google BigQuery. · Cloud Knowledge: AWS/Azure/GCP · Dev Ops (Knowledge): Kubernetes, Docker, FluentD, Kibana, Grafana, Prometheus · Cloud Certifications (Bonus): AWS Professional Solution Architect, AWS Machine Learning Specialty, Azure Solutions Architect Expert · Proficient in Python, SQL, Javascript Mandatory skill sets: ML Pipeline Design: · • Design ML pipelines for experiment management, model management, feature management, and model retraining. • · Design APIs for model inferencing at scale. · Proven expertise with MLflow, SageMaker, Vertex AI, and Azure AI. • · LLM Serving and GPU Architecture: · Possess deep knowledge of GPU architectures. • · Expertise in distributed training and serving of large language models. • · Proficient in model and data parallel training using frameworks like DeepSpeed and service frameworks like vLLM. • · Model Fine-Tuning and Optimization: · Demonstrate proven expertise in model fine-tuning and optimization techniques. • · Achieve better latencies and accuracies in model results. · Reduce training and resource requirements for fine-tuning LLM and LVM models. · DevOps and LLMOps Proficiency: · Proven expertise in DevOps and LLMOps practices. • · Knowledgeable in Kubernetes, Docker, and container orchestration. • · Deep understanding of LLM orchestration frameworks like Flowise, Langflow, and Langgraph. • · Skill Matrix · LLM: Hugging Face OSS LLMs, GPT, Gemini, Claude, Mixtral, Llama • · LLM Ops: ML Flow, Langchain, Langraph, LangFlow, Flowise, LLamaIndex, SageMaker, AWS Bedrock, Vertex AI, Azure AI · • Databases/Datawarehouse: DynamoDB, Cosmos, MongoDB, RDS, MySQL, PostGreSQL, Aurora, Spanner, Google BigQuery. · Cloud Knowledge: AWS/Azure/GCP · Dev Ops (Knowledge): Kubernetes, Docker, FluentD, Kibana, Grafana, Prometheus · Cloud Certifications (Bonus): AWS Professional Solution Architect, AWS Machine Learning Specialty, Azure Solutions Architect Expert · Proficient in Python, SQL, Javascript Preferred skill sets: ML Pipeline Design: · • Design ML pipelines for experiment management, model management, feature management, and model retraining. • · Design APIs for model inferencing at scale. · Proven expertise with MLflow, SageMaker, Vertex AI, and Azure AI. • · LLM Serving and GPU Architecture: · Possess deep knowledge of GPU architectures. • · Expertise in distributed training and serving of large language models. • · Proficient in model and data parallel training using frameworks like DeepSpeed and service frameworks like vLLM. • · Model Fine-Tuning and Optimization: · Demonstrate proven expertise in model fine-tuning and optimization techniques. • · Achieve better latencies and accuracies in model results. · Reduce training and resource requirements for fine-tuning LLM and LVM models. · DevOps and LLMOps Proficiency: · Proven expertise in DevOps and LLMOps practices. • · Knowledgeable in Kubernetes, Docker, and container orchestration. • · Deep understanding of LLM orchestration frameworks like Flowise, Langflow, and Langgraph. • · Skill Matrix · LLM: Hugging Face OSS LLMs, GPT, Gemini, Claude, Mixtral, Llama • · LLM Ops: ML Flow, Langchain, Langraph, LangFlow, Flowise, LLamaIndex, SageMaker, AWS Bedrock, Vertex AI, Azure AI · • Databases/Datawarehouse: DynamoDB, Cosmos, MongoDB, RDS, MySQL, PostGreSQL, Aurora, Spanner, Google BigQuery. · Cloud Knowledge: AWS/Azure/GCP · Dev Ops (Knowledge): Kubernetes, Docker, FluentD, Kibana, Grafana, Prometheus · Cloud Certifications (Bonus): AWS Professional Solution Architect, AWS Machine Learning Specialty, Azure Solutions Architect Expert · Proficient in Python, SQL, Javascript Years of experience required: 12-16 years Education qualification: B.Tech/MCA/BCA/M.tech Education (if blank, degree and/or field of study not specified) Degrees/Field of Study required: Bachelor of Technology Degrees/Field of Study preferred: Certifications (if blank, certifications not specified) Required Skills Java (Programming Language) Optional Skills Accepting Feedback, Accepting Feedback, Active Listening, AI Fluency, AI-Human Collaboration, Algorithm Development, Alteryx (Automation Platform), Analytical Thinking, Analytic Research, Big Data, Business Data Analytics, Coaching and Feedback, 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 {+ 46 more} Desired Languages (If blank, desired languages not specified) Travel Requirements Not Specified Available for Work Visa Sponsorship? No Government Clearance Required? No Job Posting End Date September 11, 2026