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: 8-16 years
Education qualification: B.Tech/MCA/BCA/M.tech
