Description

• Design and implement scalable Generative AI solutions using RAG architectures.
• Develop AI-powered applications leveraging LLMs such as GPT, Claude, Llama, and Gemini.
• Build end-to-end data ingestion, embedding, indexing, retrieval, and response-generation pipelines.
• Develop and optimize prompt engineering strategies for improved model performance.
• Integrate vector databases and semantic search capabilities into AI applications.
• Collaborate with cross-functional teams to translate business requirements into AI-driven solutions.
• Monitor, evaluate, and improve model accuracy, relevance, latency, and cost efficiency.
• Implement security, governance, and Responsible AI best practices.
• Deploy and maintain GenAI solutions in cloud environments.

Required Skills:

• Strong experience with Generative AI and RAG Frameworks.
• Expertise in Python programming.
• Hands-on experience with LangChain, LlamaIndex, or similar orchestration frameworks.
• Experience working with LLMs and foundation models.
• Knowledge of Prompt Engineering and AI model evaluation.
• Experience with Vector Databases such as Pinecone, ChromaDB, Weaviate, FAISS, or Azure AI Search.
• Strong understanding of Embeddings, Semantic Search, and Information Retrieval techniques.
• Experience with REST APIs, Microservices, and Cloud Platforms (Azure, AWS, or GCP).
• Familiarity with Docker, Kubernetes, CI/CD, and DevOps practices.

Preferred Qualifications

• Experience with AI Agents and Agentic AI frameworks.
• Exposure to GraphRAG, Knowledge Graphs, and Advanced Retrieval Techniques.
• Experience in MLOps and AI Governance.
• Knowledge of Responsible AI and Model Monitoring frameworks.
• Experience developing enterprise-grade AI applications in large-scale environments

Key Skills

Education

Bachelor's degree