Hiring for Returnship
Only for Female Candidates.
JD.
Experience - 5-10years
Level - TM/SDM
Location - Pan India
NP - Immediate
GenAI or GenAI Architect
Build GenAI applications using Python for tasks like chatbots, summarization and intelligent automation.
- Develop and fine tune LLMs and ML models for classification, prediction, and decision support.
- Design solutions using embeddings, vector search, and retrieval augmented generation (RAG).
- Deploy models using Azure Machine Learning and Azure OpenAI scale with Azure Functions and Cognitive Services.
- Integrate models with AWS services like SageMaker, Lambda, Bedrock and data platforms like Snowflake.
- Integrate AI systems with APIs, enterprise data platforms and business workflows.
Strong Python development with experience in GenAI frameworks like LangChain, Hugging Face, OpenAI.
- A. LLMs and hyperparameters (Azure / AWS / GCP / Open Source)
- B. Embedding models and vector database knowledge
- C. Prompting Techniques (Zero shot, few shot, chain of thought)
- D. Frameworks: Langchain, Pydantic
- E. RAG, Problem solving skills on where to apply RAG / Other Gen AI techniques.
- B. Frameworks like Pandas, Fast API, Numpy
Preferred Skills
- Solid foundation in ML algorithms, training pipelines and evaluation techniques.
- Familiarity with prompt engineering, tokenization and model optimization.
- Hands-on with Azure cloud tools for model lifecycle, deployment and serverless execution.
- Ability to connect models to data sources, automation tools and orchestration platforms.
System Design: Develop and design the architecture for AI systems, ensuring they integrate seamlessly with business operations.
- Technology Selection: Choose appropriate technologies and tools for building and deploying generative AI solutions.
- Scalability: Ensure the AI systems are scalable and can handle increasing workloads efficiently.
- Model Management: Oversee the lifecycle of generative AI models, including development, deployment, and maintenance.
- Prompt Engineering: Design and refine prompts used in natural language processing models to optimize performance.
- Data Integration: Integrate data from various sources to support AI model training and inference.
- Performance Optimization: Continuously monitor and optimize the performance of AI models and systems.
- Security and Compliance: Ensure AI systems adhere to security protocols and compliance standards.
- Collaboration: Work closely with data scientists, ML engineers, and other stakeholders to align AI solutions with business goals.
- Innovation: Stay updated with the latest advancements in AI and incorporate innovative solutions into the architecture.
