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.

Key Responsibilities

• 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.