Chennai, Tamil Nadu, India
Information Technology
Full-Time
Awign
Overview
Title : GenAI Engineer
Location : Bengaluru, Chennai, Hyderabad, Pune, Vadodara, Gujarat
Years of Exp : 7 to 12 Years
Employment Type : Full-time
Key Responsibilities
Location : Bengaluru, Chennai, Hyderabad, Pune, Vadodara, Gujarat
Years of Exp : 7 to 12 Years
Employment Type : Full-time
Key Responsibilities
- Architect and develop Generative AI-based applications using Azure OpenAI, Azure ML Studio, Azure Cognitive Search, and Azure Document Intelligence.
- Build and optimize Retrieval-Augmented Generation (RAG) pipelines for enterprise search, knowledge management, and domain-specific Q&A systems.
- Integrate Large Language Models (LLMs) into production applications with attention to performance, scalability, and governance.
- Design intelligent workflows using LangChain, LlamaIndex, and Semantic Kernel for orchestrating multi-step AI interactions and agent behaviors.
- Collaborate with data scientists, ML engineers, and DevOps to deploy, monitor, and manage cloud-native AI solutions.
- Incorporate OCR capabilities and document understanding pipelines using Azure Form Recognizer and Document Intelligence.
- Ensure AI models and pipelines comply with enterprise standards, data privacy, and responsible AI principles.
- Lead proof-of-concepts (PoCs), rapid prototypes, and solution demos to validate GenAI use cases across departments.
- Maintain a strong understanding of evolving LLM architectures, foundation models, and emerging GenAI techniques.
- Contribute to technical documentation, best practices, and reusable solution accelerators.
- Bachelor's or Masters degree in Computer Science, Artificial Intelligence, Data Science, or a related field.
- Azure OpenAI
- Azure Machine Learning Studio
- Azure Cognitive Search
- Azure Form Recognizer / Document Intelligence
- Strong Python development skills, especially in the context of LLM pipelines and prompt engineering.
- Expertise with GenAI libraries and tools such as:
- LangChain
- LlamaIndex
- Semantic Kernel
- Familiarity with RAG architecture, vector databases (e.g., FAISS, Redis, Azure Cognitive Search), and embedding models.
- Experience with containerization (Docker) and cloud-native deployment pipelines using Azure DevOps, Kubernetes, or Terraform.
- Understanding of Responsible AI principles, including bias mitigation, explainability, and model monitoring.
- Excellent problem-solving and communication skills; ability to work in cross-functional agile teams.
- Experience integrating agent-based frameworks for autonomous task execution and multi-agent collaboration.
- Knowledge of other GenAI ecosystems like Hugging Face Transformers, Anthropic Claude, or Mistral models.
- Background in enterprise content management, knowledge graphs, or intelligent document processing (IDP).
- Microsoft Certified: Azure AI Engineer Associate
- Microsoft Certified: Azure Solutions Architect Expert
- Opportunity to work on groundbreaking AI initiatives at enterprise scale.
- Collaborative, fast-paced, and innovation-driven team culture.
- Flexible work arrangements (remote/hybrid).
- Competitive compensation, equity options, and comprehensive benefits.
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