Gen AI Engineer

KEY RESPONSIBILITIES • Design, build, and deploy end-to-end Generative AI applications using LLMs, vision-language models, and multimodal AI. • Proficiency in Python with strong experience using GenAI/LLM frameworks such as LangChain, LlamaIndex, CrewAI, or HuggingFace Transformers. • Develop applications using OpenAI, Azure OpenAI, or custom-deployed open-source models (e.g., LLaMA, DeepSeek, Mistral). • Create and manage agent-based architectures for task orchestration using frameworks like AutoGen, CrewAI, or Semantic Kernel. • Implement Retrieval-Augmented Generation (RAG) systems using FAISS, Weaviate, or Azure Cognitive Search. • Work with document loaders, chunking strategies, vector embeddings, and prompt engineering techniques for optimal model performance. • Build and deploy AI-powered apps using FastAPI (backend), React/Streamlit/Gradio (frontend), and PostgreSQL or vector DBs (Pinecone, Qdrant). • Design and operationalize GenAI workflows using Docker, Kubernetes, and MLOps tools (e.g., MLFlow, Azure ML). • Integrate AI with cloud-native services (e.g., Azure Functions, EventHub, Cosmos DB, OneLake, Azure Fabric). • Continuously improve model accuracy and relevance through fine-tuning, prompt tuning, and response evaluation techniques. • Ensure alignment of GenAI solutions with product goals and compliance with security, governance, and data privacy standards. • Evaluate trade-offs across different model providers (OpenAI, Azure, Anthropic, open-source) based on latency, cost, accuracy, and IP risk. • Collaborate with cross-functional teams including Product, Data Engineering, and QA to ensure robust deployment of GenAI features. • Implement logging, monitoring, and feedback loops to support performance tuning and hallucination mitigation. PROFESSIONAL EXPERIENCE/QUALIFICATIONS • Bachelor’s degree in Computer Science, AI/ML, Data Science, or related field. • 5–7 years of experience in AI/ML Engineering with at least 1–2 years focused on Generative AI solutions. • Strong understanding of large language models, embeddings, tokenization, and attention mechanisms. • Experience with GenAI deployment in enterprise environments using tools like Azure OpenAI, HuggingFace, or Ollama. • Deep knowledge of prompt engineering, function calling, system prompts, and guardrail frameworks. • Familiarity with real-time data pipelines, vector search, and integrating AI agents with enterprise APIs. • Experience building and managing containerized workloads (Docker, Kubernetes). • Strong problem-solving skills and ability to design scalable, production-grade GenAI solutions.

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