Generative AI Engineer

Overview

We are seeking an experienced Generative AI Engineer to join our team in Noida on a full-time basis. This position demands profound knowledge in AI and software engineering, focusing on building advanced Generative AI solutions tailored for enterprise needs.

Key Responsibilities

• Architect, develop, and deploy Generative AI applications leveraging large language models (LLMs) and foundational AI models.
• Construct and enhance retrieval-augmented generation (RAG) applications, semantic search systems, and AI-driven chatbot solutions.
• Create intelligent AI agents and automate workflows utilizing contemporary orchestration frameworks.
• Fine-tune, assess, and roll out foundation models aligned with organizational use cases.
• Integrate generative AI capabilities with existing enterprise software, APIs, and cloud infrastructures.
• Formulate strategies for prompt engineering and develop methodologies for model evaluation to boost accuracy and efficiency.
• Apply responsible AI methodologies, including governance, security measures, and compliance adherence.
• Collaborate closely with stakeholders, architects, data scientists, and engineering teams to identify AI-driven opportunities.
• Continuously monitor model performance to optimize inference costs and scalability.
• Stay updated with the latest trends, tools, and technological advancements in generative AI.

Required Skills & Experience

• A total professional experience of 6 to 10 years with a minimum of 2 to 4 years dedicated to AI, machine learning, and generative AI projects.
• Proficiency in Python programming and familiarity working with RESTful APIs and microservices architecture.
• Hands-on experience with LLM platforms such as OpenAI, Azure OpenAI, Anthropic Claude, Gemini, or similar.
• Knowledge and expertise in RAG structures and experience with vector databases like Pinecone, ChromaDB, FAISS, Weaviate, or Azure AI Search.
• Working experience with frameworks including LangChain, LangGraph, LlamaIndex, Semantic Kernel, CrewAI, AutoGen, or equivalent technologies.
• Strong familiarity with prompt engineering, model assessment, agentic AI, and systems involving multiple AI agents.
• Experience with machine learning frameworks such as TensorFlow, PyTorch, and Hugging Face Transformers.
• Comfortable working with cloud platforms, primarily Azure (preferred), but also AWS or Google Cloud Platform.
• Hands-on experience with CI/CD pipelines, containerization tools like Docker, orchestration platforms including Kubernetes or OpenShift, and MLOps methodologies.
• Solid understanding of data engineering principles, embeddings, vector search mechanics, and knowledge graph technologies.
• Excellent analytical thinking, communication capabilities, and experience managing stakeholder expectations.

Preferred Qualifications

• Background or experience in sectors like financial services, banking, insurance, or other regulated industries.
• Expertise in Responsible AI practices, AI governance, model risk oversight, and adherence to compliance frameworks.
• Experience handling both structured and unstructured enterprise datasets.
• Exposure to AI observability techniques and monitoring solutions.
• Relevant certifications related to Azure AI, machine learning, or cloud technologies.

Additional Advantages

• Experience working with multimodal AI applications involving text, imagery, and voice modalities.
• Familiarity with GraphRAG and knowledge graph-based implementations.
• Experience in fine-tuning or working with open-source models like Llama, Mistral, Falcon, or Phi.
• Building enterprise copilots and conversational AI platforms is a plus.

Educational Qualifications

A bachelor's or master's degree in computer science, information technology, artificial intelligence, data science, or related disciplines is required.

Technologies Utilized

This role extensively involves Python, generative AI solutions, LLMs, Azure OpenAI, OpenAI platforms, RAG models, LangChain, LangGraph, LlamaIndex, vector databases, semantic search, AI agents, prompt engineering, cloud solutions like Azure and AWS, Kubernetes, Docker, MLOps practices, Hugging Face libraries, PyTorch, and TensorFlow.