4 Wrenai Jobs

The Technical Lead will focus on the development, implementation, and engineering of GenAI applications using the latest LLMs and frameworks. This role requires hands-on expertise in Python programming, cloud platforms, and advanced AI techniques, along with additional skills in front-end technologies, data modernization, and API integration. The Technical Lead will be responsible for building applications from the ground up, ensuring robust, scalable, and efficient solutions. Key Responsibilities: Application Development: Build GenAI applications from scratch using frameworks like Autogen, Crew.ai, LangGraph, LlamaIndex, and LangChain. Python Programming: Develop high-quality, efficient, and maintainable Python code for GenAI solutions. Large-Scale Data Handling & Architecture: Design and implement architectures for handling large-scale structured and unstructured data. Multi-Modal LLM Applications: Familiarity with text chat completion, vision, and speech models. Fine-tune SLM(Small Language Model) for domain specific data and use cases. Front-End Integration: Implement user interfaces using front-end technologies like React, Streamlit, and AG Grid, ensuring seamless integration with GenAI backends. Data Modernization and Transformation: Design and implement data modernization and transformation pipelines to support GenAI applications. OCR and Document Intelligence: Develop solutions for Optical Character Recognition (OCR) and document intelligence using cloud-based tools. API Integration: Use REST, SOAP, and other protocols to integrate APIs for data ingestion, processing, and output delivery. Cloud Platform Expertise: Leverage Azure, GCP, and AWS for deploying and managing GenAI applications. Fine-Tuning LLMs: Apply fine-tuning techniques such as PEFT, QLoRA, and LoRA to optimize LLMs for specific use cases. LLMOps Implementation: Set up and manage LLMOps pipelines for continuous integration, deployment, and monitoring. Responsible AI Practices: Ensure ethical AI practices are embedded in the development process. RAG and Modular RAG : Implement Retrieval-Augmented Generation (RAG) and Modular RAG architectures for enhanced model performance. Data Curation Automation : Build tools and pipelines for automated data curation and preprocessing. Technical Documentation : Create detailed technical documentation for developed applications and processes. Collaboration : Work closely with cross-functional teams, including data scientists, engineers, and product managers, to deliver high-impact solutions. Mentorship : Guide and mentor junior developers, fostering a culture of technical excellence and innovation. Required Skills : Python Programming : Deep expertise in Python for building GenAI applications and automation tools. Productionization of GenAI application beyond PoCs Using scale frameworks and tools such as Pylint,Pyritetc. LLM Frameworks : Proficiency in frameworks like Autogen, Crew.ai, LangGraph, LlamaIndex, and LangChain. Large-Scale Data Handling & Architecture: Design and implement architectures for handling large-scale structured and unstructured data. Multi-Modal LLM Applications: Familiarity with text chat completion, vision, and speech models. Fine-tune SLM(Small Language Model) for domain specific data and use cases. Prompt injection fallback and RCE tools such as Pyrit and HAX toolkit etc. Anti-hallucination and anti-gibberish tools such as Bleu etc. Front-End Technologies : Strong knowledge of React, Streamlit, AG Grid, and JavaScript for front-end development. Cloud Platforms : Extensive experience with Azure, GCP, and AWS for deploying and managing GenAI applications. Fine-Tuning Techniques : Mastery of PEFT, QLoRA, LoRA, and other fine-tuning methods. LLMOps : Strong knowledge of LLMOps practices for model deployment, monitoring, and management. Responsible AI : Expertise in implementing ethical AI practices and ensuring compliance with regulations. RAG and Modular RAG : Advanced skills in Retrieval-Augmented Generation and Modular RAG architectures. Data Modernization : Expertise in modernizing and transforming data for GenAI applications. OCR and Document Intelligence : Proficiency in OCR and document intelligence using cloud-based tools. API Integration : Experience with REST, SOAP, and other protocols for API integration. Data Curation : Expertise in building automated data curation and preprocessing pipelines. Technical Documentation : Ability to create clear and comprehensive technical documentation. Collaboration and Communication : Strong collaboration and communication skills to work effectively with cross-functional teams. Mentorship : Proven ability to mentor junior developers and foster a culture of technical excellence.

The Implementation Technical Architect will be responsible for designing, developing, and deploying cutting-edge Generative AI (GenAI) solutions using the latest Large Language Models (LLMs) and frameworks. This role requires deep expertise in Python programming, cloud platforms (Azure, GCP, AWS), and advanced AI techniques such as fine-tuning, LLMOps, and Responsible AI. The architect will lead the development of scalable, secure, and efficient GenAI applications, ensuring alignment with business goals and technical requirements. Key Responsibilities: Design and Architecture: Create scalable and modular architecture for GenAI applications using frameworks like Autogen, Crew.ai, LangGraph, LlamaIndex, and LangChain. Python Development: Lead the development of Python-based GenAI applications, ensuring high-quality, maintainable, and efficient code. Data Curation Automation: Build tools and pipelines for automated data curation, preprocessing, and augmentation to support LLM training and fine-tuning. Cloud Integration: Design and implement solutions leveraging Azure, GCP, and AWS LLM ecosystems, ensuring seamless integration with existing cloud infrastructure. Fine-Tuning Expertise: Apply advanced fine-tuning techniques such as PEFT, QLoRA, and LoRA to optimize LLM performance for specific use cases. LLMOps Implementation: Establish and manage LLMOps pipelines for continuous integration, deployment, and monitoring of LLM-based applications. Responsible AI: Ensure ethical AI practices by implementing Responsible AI principles, including fairness, transparency, and accountability. RLHF and RAG: Implement Reinforcement Learning with Human Feedback (RLHF) and Retrieval-Augmented Generation (RAG) techniques to enhance model performance. Modular RAG Design: Develop and optimize Modular RAG architectures for complex GenAI applications. Open-Source Collaboration: Leverage Hugging Face and other open-source platforms for model development, fine-tuning, and deployment. Front-End Integration: Collaborate with front-end developers to integrate GenAI capabilities into user-friendly interfaces. SDLC and DevSecOps: Implement secure software development lifecycle (SDLC) and DevSecOps practices tailored to LLM-based projects. Technical Documentation: Create detailed design artifacts, technical specifications, and architecture diagrams for complex projects. Stakeholder Collaboration: Work closely with cross-functional teams, including data scientists, engineers, and product managers, to deliver high-impact solutions. Mentorship and Leadership: Guide and mentor junior developers and engineers, fostering a culture of innovation and technical excellence. Required Skills: Python Programming: Deep expertise in Python for building GenAI applications and automation tools. LLM Frameworks: Proficiency in frameworks like Autogen, Crew.ai, LangGraph, LlamaIndex, and LangChain. Large-Scale Data Handling & Architecture: Design and implement architectures for handling large-scale structured and unstructured data. Multi-Modal LLM Applications: Familiarity with text chat completion, vision, and speech models. Fine-tune SLM(Small Language Model) for domain specific data and use cases. Prompt injection fallback and RCE tools such as Pyrit and HAX toolkit etc. Anti-hallucination and anti-gibberish tools such as Bleu etc. Cloud Platforms: Extensive experience with Azure, GCP, and AWS LLM ecosystems and APIs. Fine-Tuning Techniques: Mastery of PEFT, QLoRA, LoRA, and other fine-tuning methods. LLMOps: Strong knowledge of LLMOps practices for model deployment, monitoring, and management. Responsible AI: Expertise in implementing ethical AI practices and ensuring compliance with regulations. RLHF and RAG: Advanced skills in Reinforcement Learning with Human Feedback and Retrieval-Augmented Generation. Modular RAG: Deep understanding of Modular RAG architectures and their implementation. Hugging Face: Proficiency in using Hugging Face and similar open-source platforms for model development. Front-End Integration: Knowledge of front-end technologies to enable seamless integration of GenAI capabilities. SDLC and DevSecOps: Strong understanding of secure software development lifecycle and DevSecOps practices for LLMs. Data Curation: Expertise in building automated data curation and preprocessing pipelines. API Development: Experience in designing and implementing APIs for GenAI applications. Technical Documentation: Ability to create clear and comprehensive design artifacts and technical documentation. Leadership and Mentorship: Proven ability to lead teams, mentor junior developers, and drive technical innovation.

The Implementation Technical Architect will be responsible for designing, developing, and deploying cutting-edge Generative AI (GenAI) solutions using the latest Large Language Models (LLMs) and frameworks. This role requires deep expertise in Python programming, cloud platforms (Azure, GCP, AWS), and advanced AI techniques such as fine-tuning, LLMOps, and Responsible AI. The architect will lead the development of scalable, secure, and efficient GenAI applications, ensuring alignment with business goals and technical requirements. Key Responsibilities: Design and Architecture: Create scalable and modular architecture for GenAI applications using frameworks like Autogen, Crew.ai, LangGraph, LlamaIndex, and LangChain. Python Development: Lead the development of Python-based GenAI applications, ensuring high-quality, maintainable, and efficient code. Data Curation Automation: Build tools and pipelines for automated data curation, preprocessing, and augmentation to support LLM training and fine-tuning. Cloud Integration: Design and implement solutions leveraging Azure, GCP, and AWS LLM ecosystems, ensuring seamless integration with existing cloud infrastructure. Fine-Tuning Expertise: Apply advanced fine-tuning techniques such as PEFT, QLoRA, and LoRA to optimize LLM performance for specific use cases. LLMOps Implementation: Establish and manage LLMOps pipelines for continuous integration, deployment, and monitoring of LLM-based applications. Responsible AI: Ensure ethical AI practices by implementing Responsible AI principles, including fairness, transparency, and accountability. RLHF and RAG: Implement Reinforcement Learning with Human Feedback (RLHF) and Retrieval-Augmented Generation (RAG) techniques to enhance model performance. Modular RAG Design: Develop and optimize Modular RAG architectures for complex GenAI applications. Open-Source Collaboration: Leverage Hugging Face and other open-source platforms for model development, fine-tuning, and deployment. Front-End Integration: Collaborate with front-end developers to integrate GenAI capabilities into user-friendly interfaces. SDLC and DevSecOps: Implement secure software development lifecycle (SDLC) and DevSecOps practices tailored to LLM-based projects. Technical Documentation: Create detailed design artifacts, technical specifications, and architecture diagrams for complex projects. Stakeholder Collaboration: Work closely with cross-functional teams, including data scientists, engineers, and product managers, to deliver high-impact solutions. Mentorship and Leadership: Guide and mentor junior developers and engineers, fostering a culture of innovation and technical excellence. Required Skills: Python Programming: Deep expertise in Python for building GenAI applications and automation tools. LLM Frameworks: Proficiency in frameworks like Autogen, Crew.ai, LangGraph, LlamaIndex, and LangChain. Large-Scale Data Handling & Architecture: Design and implement architectures for handling large-scale structured and unstructured data. Multi-Modal LLM Applications: Familiarity with text chat completion, vision, and speech models. Fine-tune SLM(Small Language Model) for domain specific data and use cases. Prompt injection fallback and RCE tools such as Pyrit and HAX toolkit etc. Anti-hallucination and anti-gibberish tools such as Bleu etc. Cloud Platforms: Extensive experience with Azure, GCP, and AWS LLM ecosystems and APIs. Fine-Tuning Techniques: Mastery of PEFT, QLoRA, LoRA, and other fine-tuning methods. LLMOps: Strong knowledge of LLMOps practices for model deployment, monitoring, and management. Responsible AI: Expertise in implementing ethical AI practices and ensuring compliance with regulations. RLHF and RAG: Advanced skills in Reinforcement Learning with Human Feedback and Retrieval-Augmented Generation. Modular RAG: Deep understanding of Modular RAG architectures and their implementation. Hugging Face: Proficiency in using Hugging Face and similar open-source platforms for model development. Front-End Integration: Knowledge of front-end technologies to enable seamless integration of GenAI capabilities. SDLC and DevSecOps: Strong understanding of secure software development lifecycle and DevSecOps practices for LLMs. Data Curation: Expertise in building automated data curation and preprocessing pipelines. API Development: Experience in designing and implementing APIs for GenAI applications. Technical Documentation: Ability to create clear and comprehensive design artifacts and technical documentation. Leadership and Mentorship: Proven ability to lead teams, mentor junior developers, and drive technical innovation.

The Technical Lead will focus on the development, implementation, and engineering of GenAI applications using the latest LLMs and frameworks. This role requires hands-on expertise in Python programming, cloud platforms, and advanced AI techniques, along with additional skills in front-end technologies, data modernization, and API integration. The Technical Lead will be responsible for building applications from the ground up, ensuring robust, scalable, and efficient solutions. Key Responsibilities: Application Development: Build GenAI applications from scratch using frameworks like Autogen, Crew.ai, LangGraph, LlamaIndex, and LangChain. Python Programming: Develop high-quality, efficient, and maintainable Python code for GenAI solutions. Large-Scale Data Handling & Architecture: Design and implement architectures for handling large-scale structured and unstructured data. Multi-Modal LLM Applications: Familiarity with text chat completion, vision, and speech models. Fine-tune SLM(Small Language Model) for domain specific data and use cases. Front-End Integration: Implement user interfaces using front-end technologies like React, Streamlit, and AG Grid, ensuring seamless integration with GenAI backends. Data Modernization and Transformation: Design and implement data modernization and transformation pipelines to support GenAI applications. OCR and Document Intelligence: Develop solutions for Optical Character Recognition (OCR) and document intelligence using cloud-based tools. API Integration: Use REST, SOAP, and other protocols to integrate APIs for data ingestion, processing, and output delivery. Cloud Platform Expertise: Leverage Azure, GCP, and AWS for deploying and managing GenAI applications. Fine-Tuning LLMs: Apply fine-tuning techniques such as PEFT, QLoRA, and LoRA to optimize LLMs for specific use cases. LLMOps Implementation: Set up and manage LLMOps pipelines for continuous integration, deployment, and monitoring. Responsible AI Practices: Ensure ethical AI practices are embedded in the development process. RAG and Modular RAG : Implement Retrieval-Augmented Generation (RAG) and Modular RAG architectures for enhanced model performance. Data Curation Automation : Build tools and pipelines for automated data curation and preprocessing. Technical Documentation : Create detailed technical documentation for developed applications and processes. Collaboration : Work closely with cross-functional teams, including data scientists, engineers, and product managers, to deliver high-impact solutions. Mentorship : Guide and mentor junior developers, fostering a culture of technical excellence and innovation. Required Skills : Python Programming : Deep expertise in Python for building GenAI applications and automation tools. Productionization of GenAI application beyond PoCs Using scale frameworks and tools such as Pylint,Pyritetc. LLM Frameworks : Proficiency in frameworks like Autogen, Crew.ai, LangGraph, LlamaIndex, and LangChain. Large-Scale Data Handling & Architecture: Design and implement architectures for handling large-scale structured and unstructured data. Multi-Modal LLM Applications: Familiarity with text chat completion, vision, and speech models. Fine-tune SLM(Small Language Model) for domain specific data and use cases. Prompt injection fallback and RCE tools such as Pyrit and HAX toolkit etc. Anti-hallucination and anti-gibberish tools such as Bleu etc. Front-End Technologies : Strong knowledge of React, Streamlit, AG Grid, and JavaScript for front-end development. Cloud Platforms : Extensive experience with Azure, GCP, and AWS for deploying and managing GenAI applications. Fine-Tuning Techniques : Mastery of PEFT, QLoRA, LoRA, and other fine-tuning methods. LLMOps : Strong knowledge of LLMOps practices for model deployment, monitoring, and management. Responsible AI : Expertise in implementing ethical AI practices and ensuring compliance with regulations. RAG and Modular RAG : Advanced skills in Retrieval-Augmented Generation and Modular RAG architectures. Data Modernization : Expertise in modernizing and transforming data for GenAI applications. OCR and Document Intelligence : Proficiency in OCR and document intelligence using cloud-based tools. API Integration : Experience with REST, SOAP, and other protocols for API integration. Data Curation : Expertise in building automated data curation and preprocessing pipelines. Technical Documentation : Ability to create clear and comprehensive technical documentation. Collaboration and Communication : Strong collaboration and communication skills to work effectively with cross-functional teams. Mentorship : Proven ability to mentor junior developers and foster a culture of technical excellence.