Our Client Currently looking for Generative AI Engineer/Architect
Job Description :
– 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.
Key Responsibility Areas :
1. System Design : Develop and design the architecture for AI systems, ensuring they integrate seamlessly with business operations.
2. Technology Selection : Choose appropriate technologies and tools for building and deploying generative AI solutions.
3. Scalability : Ensure the AI systems are scalable and can handle increasing workloads efficiently.
4. Model Management : Oversee the lifecycle of generative AI models, including development, deployment, and maintenance.
5. Prompt Engineering : Design and refine prompts used in natural language processing models to optimize performance.
6. Data Integration : Integrate data from various sources to support AI model training and inference.
7. Performance Optimization : Continuously monitor and optimize the performance of AI models and systems.
8. Security and Compliance : Ensure AI systems adhere to security protocols and compliance standards.
9. Collaboration : Work closely with data scientists, ML engineers, and other stakeholders to align AI solutions with business goals.
10. Innovation : Stay updated with the latest advancements in AI and incorporate innovative solutions into the architecture.
Are you interested in this position?
Apply by clicking on the “Apply Now” button below!
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