Line of Service

Advisory

Industry/Sector

Not Applicable

Specialism

Data, Analytics & AI

Management Level

Director

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 business intelligence at PwC, you will focus on leveraging data and analytics to provide strategic insights and drive informed decision-making for clients. You will develop and implement innovative solutions to optimise business performance and enhance competitive advantage.

*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 Skilled 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: 8-16 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 Engineering, Master of Business Administration, Bachelor of Technology

Degrees/Field of Study preferred:

Certifications (if blank, certifications not specified)

Required Skills

AI Architecture

Optional Skills

Accepting Feedback, Accepting Feedback, Active Listening, Analytical Thinking, Applied Macroeconomics, Business Case Development, Business Data Analytics, Business Intelligence and Reporting Tools (BIRT), Business Intelligence Development Studio, Coaching and Feedback, Communication, Competitive Advantage, Continuous Process Improvement, Creativity, Data Analysis and Interpretation, Data Architecture Development, Database Management System (DBMS), Data Collection, Data Pipeline, Data Quality, Data Science, Data Visualization, Embracing Change, Emotional Regulation, Empathy {+ 36 more}

Desired Languages (If blank, desired languages not specified)

Travel Requirements

Up to 60%

Available for Work Visa Sponsorship?

No

Government Clearance Required?

No

Job Posting End Date

September 9, 2026

📌 IN_Director_AI Architect_Data and Analytics_Advisory_Bangalore (India)
🏢 PwC
📍 India