4 Finetuning Techniques Jobs

As a Senior Developer for GenAI Applications, your primary focus will be on coding, developing, and implementing GenAI applications using fine-tuned Large Language Models (LLMs) and Small Language Models (SLMs). In this role, you will utilize your hands-on expertise in open-source frameworks, libraries, and cloud tools to prototype and demonstrate innovative GenAI solutions under the guidance of senior team members. Your contributions will be essential in delivering high-quality, scalable, and cutting-edge applications. Responsibilities: - Write clean, efficient, and maintainable code for GenAI applications using Python and various open-source frameworks such as autogen and crew.ai. - Fine-tune LLMs and SLMs using techniques like PEFT, LoRA, and QLoRA to cater to specific use cases. - Collaborate with frameworks like Hugging Face, LangChain, LlamaIndex, and others to develop GenAI solutions. - Deploy and manage GenAI models and applications on cloud platforms such as Azure, GCP, and AWS. - Quickly prototype and demonstrate GenAI applications to showcase capabilities and gather feedback. - Build and maintain data preprocessing pipelines for training and fine-tuning models. - Integrate REST, SOAP, and other APIs for data ingestion, processing, and output delivery. - Evaluate model performance using metrics and benchmarks, and continuously iterate to enhance results. - Develop solutions for Optical Character Recognition (OCR) and document intelligence using open-source and cloud-based tools. - Collaborate with front-end developers to integrate GenAI capabilities into user-friendly interfaces using tools like Streamlit or React. - Utilize Git and other version control systems to manage code and collaborate effectively with team members. - Create clear and concise technical documentation for code, models, and processes. - Work closely with data scientists, engineers, and product managers to deliver impactful solutions. - Stay updated with the latest advancements in GenAI, open-source tools, and cloud technologies for continuous learning and growth. - Identify and resolve bugs, optimize code, and enhance application performance through debugging and optimization techniques. Required Skills: - Strong proficiency in Python programming, specifically in CORE Python OOP for developing GenAI applications. - Hands-on experience with fine-tuning techniques such as PEFT, LoRA, and QLoRA. - Expertise in working with open-source frameworks like Hugging Face, LangChain, LlamaIndex, and other libraries. - Familiarity with cloud platforms like Azure, GCP, and AWS for deploying and managing GenAI models. - Skills in building and maintaining data preprocessing pipelines. - Experience in API integration using protocols like REST, SOAP, and others. - Knowledge of metrics and benchmarks for evaluating model performance. - Proficiency in OCR and document intelligence using open-source and cloud tools. - Basic knowledge of front-end tools like Streamlit, React, or JavaScript for UI integration. - Proficiency in Git and version control best practices. - Ability to create clear and concise technical documentation. - Strong analytical and problem-solving skills for debugging and optimizing code. - Excellent teamwork and communication skills for effective collaboration in cross-functional teams. - Ability to quickly prototype and demonstrate GenAI applications with a growth mindset for continuous learning and staying updated with the latest GenAI trends and technologies.,

As an NLP Engineer at Tiger Analytics, you will have the opportunity to work on cutting-edge AI and analytics projects to help Fortune 1000 companies overcome their toughest challenges. You will be part of a global team of technologists and consultants dedicated to empowering businesses to achieve real outcomes and value at scale. Your role will involve collaborating with experienced team members on internal product development and client-focused projects, with a focus on the pharma domain. You will play a key role in designing, developing, and deploying GenAI solutions, with a particular emphasis on addressing challenges like hallucinations, bias, and latency. Your day-to-day responsibilities will include supporting the full lifecycle of AI project delivery, fine-tuning Large Language Models (LLMs) for specific business needs, and developing scalable pipelines for AI model deployment. You will also have the opportunity to collaborate with internal teams and external stakeholders, particularly in the pharma space, to understand business requirements and contribute to the development of tailored AI-powered systems. Additionally, you will actively participate in a collaborative environment, sharing ideas and working as part of a dynamic team of data scientists and AI engineers. To be successful in this role, you should have 7-9 years of experience in NLP, AI/ML, or data science, with a proven track record of delivering production-grade NLP & GenAI solutions. You should have deep expertise in LLMs, transformer architectures, and fine-tuning techniques, as well as strong knowledge of NLP pipelines, text preprocessing, embeddings, and named entity recognition. Experience with Agentic AI systems, LLM observability tools, and AI safety guardrails is also essential. Proficiency in Python and backend development, familiarity with MLOps and cloud platforms, and prior experience in regulated industries like life sciences or pharma are all advantageous. A problem-solving mindset, the ability to work independently, drive innovation, and mentor junior engineers are key qualities we are looking for in candidates. The compensation package for this role will be commensurate with your expertise and experience, and you will also have access to additional benefits such as health insurance, a virtual wellness platform, and knowledge communities.,

As an experienced Python programmer, you will be responsible for developing GenAI applications and automation tools using your deep expertise in Python. Your role will involve the productionization of GenAI applications beyond Proof of Concepts (PoCs) by utilizing scale frameworks and tools such as Pylint and Pyrit. In addition to Python programming, you should be proficient in working with LLM Frameworks, including Autogen, Crew.ai, LangGraph, LlamaIndex, and LangChain. Your expertise in these frameworks will be essential for enhancing GenAI applications. Moreover, you will leverage your extensive experience with cloud platforms such as Azure, GCP, and AWS to deploy and manage GenAI applications effectively. Your knowledge of at least two of these cloud platforms will be crucial for the success of the projects. You should also possess mastery of fine-tuning techniques like PEFT, QLoRA, LoRA, and other methods to optimize the performance of GenAI applications. Your understanding of these techniques will contribute to the overall efficiency of the applications. Furthermore, your strong knowledge of LLMOps practices for model deployment, monitoring, and management will ensure the seamless operation of GenAI applications. Your expertise in LLMOps will be vital for the successful deployment and maintenance of models. As part of your role, you will be required to implement responsible AI practices and ensure compliance with regulations to uphold ethical standards. Your proficiency in Responsible AI will be essential for developing applications that align with ethical guidelines. Additionally, you should have experience in API integration using protocols such as REST, SOAP, and others. Your familiarity with API integration will enable seamless communication between different systems and enhance the functionality of GenAI applications.,

The Implementation Technical Architect role focuses on designing, developing, and deploying cutting-edge Generative AI (GenAI) solutions using the latest Large Language Models (LLMs) and frameworks. Your responsibilities include creating scalable and modular architecture for GenAI applications, leading Python development for GenAI applications, building tools for automated data curation, integrating solutions with cloud platforms like Azure, GCP, and AWS, applying advanced fine-tuning techniques to optimize LLM performance, establishing LLMOps pipelines, ensuring ethical AI practices, implementing Reinforcement Learning with Human Feedback and Retrieval-Augmented Generation techniques, collaborating with front-end developers, and more. 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. 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.,