Consultant II - B2

We are seeking a skilled and innovative Generative AI Engineer with 3–6 years of experience in AI/ML development to design, build, and deploy GenAI-powered applications. The ideal candidate will have hands-on experience with Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), prompt engineering, and modern AI frameworks. You will work closely with data scientists, software engineers, and business stakeholders to deliver scalable AI solutions that drive business value.

• Design, develop, and deploy Generative AI applications using state-of-the-art LLMs.


• Build and optimize RAG pipelines leveraging vector databases and enterprise knowledge sources.


• Develop prompt engineering strategies and evaluate model performance.


• Fine-tune, customize, and optimize foundation models for domain-specific use cases.


• Integrate AI solutions with cloud platforms and enterprise systems through APIs and microservices.


• Implement AI observability, monitoring, and evaluation frameworks.


• Develop scalable and production-ready AI applications following MLOps best practices.


• Conduct experimentation and benchmarking of LLMs, embeddings, and retrieval techniques.


• Collaborate with cross-functional teams to gather requirements and translate business problems into AI solutions.


• Stay updated on advancements in Generative AI, LLMs, agents, and multimodal AI technologies.

• Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, or a related field.


• 3–6 years of experience in Software Engineering, Machine Learning, NLP, or AI development.


• Strong proficiency in Python and software engineering best practices.


• Experience with Generative AI frameworks such as:
  
  
  
  • LangChain
  
  
  • LlamaIndex
  
  
  • LangGraph
  
  
  • Semantic Kernel (preferred)
  
  


• Hands-on experience with LLMs including:
  
  
  
  • GPT models
  
  
  • Claude
  
  
  • Llama
  
  
  • Gemini
  
  
  • Mistral
  
  


• Experience building RAG systems using vector databases such as:
  
  
  
  • Pinecone
  
  
  • FAISS
  
  
  • Chroma
  
  
  • OpenSearch
  
  
  • Weaviate
  
  


• Familiarity with embedding models and semantic search techniques.