IN_Senior Associate_Gen AI_Emerging Businesses_Advisory_Bangalore

Role overview

Generative AI engineering

• Design, build, and deploy generative AI solutions using OpenAI, Anthropic, Mistral, LLaMA, Falcon, and other models.
• Fine-tune and customize foundation models using domain-specific datasets and techniques.
• Develop prompt engineering strategies for accurate and context-aware model responses.

Platforms and integration

• Implement model pipelines with Python, PyTorch, Hugging Face Transformers, and orchestration frameworks.
• Collaborate with data engineering and MLOps teams to productionize models on Azure, AWS, or GCP.
• Integrate generative AI into enterprise applications through APIs or custom interfaces.

Model quality and research

• Ensure robustness, scalability, and compliance of deployed AI models.
• Evaluate model performance with quantitative and qualitative metrics and improve outputs iteratively.
• Keep current with generative AI, foundation model, and open-source research.

Mandatory skills

• Generative AI covering large language models and transformers.
• Python, PyTorch, and Hugging Face Transformers.
• Azure, AWS, or GCP cloud platforms.
• LangChain, LangGraph, or similar orchestration frameworks.
• REST APIs with FastAPI, Flask, or comparable tools.
• MLflow or Weights & Biases, plus Git and CI/CD for ML.

Preferred skills

• RAG and vector databases such as FAISS, Pinecone, or Weaviate.
• Streamlit or Gradio for prototyping.
• Docker or Kubernetes for model deployment.
• Data preprocessing, feature engineering, and NLP libraries such as spaCy or NLTK.

Qualifications

• 4 to 7 years of experience is required.
• A degree qualification is listed as B.E., B.Tech., M.Tech., or MCA; the structured education field specifies Bachelor of Technology.

PwC Advisory context