IN_Senior Associate_Gen AI_Emerging Businesses_Advisory_Bangalore

Role Overview

Engineering Responsibilities

• Design, build, and deploy Generative AI solutions using OpenAI, Anthropic, Mistral, LLaMA, Falcon, or other open-source models.
• Fine-tune and customize foundation models using domain-specific datasets and techniques.
• Develop prompt engineering strategies to improve model accuracy and contextual responses.
• Implement model pipelines with Python, PyTorch, Hugging Face Transformers, and LangChain.
• Implement agentic AI solutions using CrewAI or LangChain.
• Integrate Generative AI into enterprise applications through APIs or custom interfaces.
• Evaluate model performance with quantitative and qualitative metrics and improve outputs through experimentation.

Cloud and Productionization

• Collaborate with data engineering and MLOps teams to productionize GenAI models on Azure, AWS, or GCP.
• Use cloud AI platforms such as Azure AI Foundry or GCP Vertex AI.
• Ensure robustness, scalability, and compliance of AI models in deployment environments.

Mandatory Qualifications

• Four to seven years of professional experience is required.
• Generative AI, LLMs, Transformers, Python, PyTorch, and Hugging Face Transformers are mandatory skills.
• Azure, AWS, or GCP deployment experience is mandatory.
• LangChain, LangGraph, or similar orchestration framework experience is mandatory.
• REST APIs, FastAPI, or Flask proficiency is mandatory.
• MLflow or Weights & Biases and CI/CD for ML are mandatory skills.

Education

• The degree and field-of-study section lists Bachelor of Technology as required.
• The education qualification section lists B.E., B.Tech., M.Tech., or MCA.

Preferred Skills

• Preferred skills include RAG, vector databases such as FAISS, Pinecone, or Weaviate, and Streamlit or Gradio.
• Preferred deployment skills include Docker and Kubernetes.
• Preferred data and NLP skills include data preprocessing, feature engineering, spaCy, NLTK, and Transformers.

PwC Advisory Context