9 Fewshot Learning Jobs

As an Associate Architect Machine Learning (ML) with 4-7 years of experience, you will play a crucial role in integrating generative AI (Gen AI) capabilities into various Software Development Lifecycle (SDLC) phases to enhance efficiency and innovation. Your primary focus will be on identifying high-impact business opportunities where Gen AI adoption can accelerate project delivery and improve solutioning across multiple domains. This position requires a blend of technical expertise, strategic thinking, and client engagement skills to successfully drive Gen AI implementation. Your responsibilities will include collaborating across multiple projects to identify Gen AI and ML strategies, leading discussions with clients to understand their pain points and recommend AI-driven solutions, actively contributing to strategy and operational meetings, and working closely with project engineering and product teams to implement Gen AI capabilities into production-ready solutions. You will also be involved in conducting proof-of-concepts (POCs) and feasibility studies, assisting teams with ML model selection, training, and deployment, and mentoring junior engineers and data scientists in best practices for ML and Gen AI development. To excel in this role, you must possess strong technical expertise in traditional ML techniques (supervised/unsupervised learning, time series forecasting, NLP) and Gen AI models (LLMs, diffusion models, transformers). Proficiency in Python, TensorFlow, PyTorch, and experience with cloud-based ML services (AWS, GCP, Azure) is essential. You should also be adept at architecting and optimizing scalable AI systems using microservices, containerization, and distributed computing for high availability and cost efficiency. Additionally, you must demonstrate strategic thinking skills by identifying and assessing new Gen AI opportunities in business processes, along with strong client handling and communication abilities to lead client discussions, presentations, and workshops effectively. Preferred qualifications for this role include prior experience in Gen AI solutioning for enterprise use cases, familiarity with LLM fine-tuning, prompt engineering, and knowledge retrieval techniques, as well as experience in deploying ML solutions in production environments at scale. Joining our team will provide you with the opportunity to be at the forefront of AI-driven innovation, collaborate with top AI/ML experts, and work on challenging projects that leverage cutting-edge AI technologies. If you are passionate about ML, Gen AI, and driving innovation, we invite you to apply and be a part of shaping the future of AI-driven solutions. Apply now to embark on this exciting journey with us.,

As a skilled AI Engineer, you will demonstrate proficiency in Generative AI & LLMs including GPT-3, Langchain, and LLMs. Your experience in prompt engineering, few-shot/zero-shot learning, RLHF, and model optimization will be essential in developing and implementing generative AI models. Your expertise in Machine Learning & NLP, particularly in Python utilizing frameworks like Hugging Face, TensorFlow, Keras, and PyTorch, will play a crucial role in optimizing LLM performance through fine-tuning and hyperparameter evaluation. A strong understanding of embeddings and data modeling is required for this role. Experience with various databases such as vector databases, RDBMS, MongoDB, and NoSQL databases (HBase, Elasticsearch), as well as data integration from multiple sources, will be beneficial. Your knowledge in Software Engineering, encompassing data structures, algorithms, system architecture, and distributed systems, will enable you to build and maintain robust distributed systems. Hands-on experience with Platform & Cloud technologies like Kubernetes, Spark ML, Databricks, and developing scalable platforms at scale is necessary. Proficiency in API Development, including API design, RESTful services, and data mapping to schemas, will be essential for integrating AI models into scalable web applications. Your responsibilities will include designing, developing, and implementing generative AI models, optimizing LLM performance, collaborating with ML and integration teams, building and maintaining distributed systems, integrating data from diverse sources, and enhancing response quality and performance using advanced techniques. To qualify for this role, you should hold a B.Tech / B.E. /M.Tech / M.E. degree and have a proven track record in building and deploying AI/ML models, particularly LLMs. Prior experience in developing public cloud services or open-source ML software would be advantageous. Knowledge of big-data tools will be considered a strong asset for this position.,

You should have a minimum of 4+ years of experience in AI-based application development. Your responsibilities will include fine-tuning pre-existing models to improve performance and accuracy. You should have experience with frameworks such as TensorFlow, PyTorch, Scikit-learn, or similar ML frameworks, as well as familiarity with APIs like OpenAI or Vertex AI. Additionally, experience with NLP tools and libraries like NLTK, SpaCy, GPT, and BERT is required. You will be implementing frameworks like LangChain, Anthropics Constitutional AI, OpenAI, Hugging Face, and Prompt Engineering techniques to build robust and scalable AI applications. Evaluating and analyzing RAG solutions and utilizing the best-in-class LLM to define customer experience solutions will be part of your role. Architecting and developing advanced generative AI solutions leveraging state-of-the-art language models (LLMs) such as GPT, LLaMA, PaLM, BLOOM, and others is also expected. You should have a strong understanding and experience with open-source multimodal LLM models to customize and create solutions. Exploring and implementing cutting-edge techniques like Few-Shot Learning, Reinforcement Learning, Multi-Task Learning, and Transfer Learning for AI model training and fine-tuning is essential. Proficiency in data preprocessing, feature engineering, and data visualization using tools like Pandas, NumPy, and Matplotlib is crucial for this role. Optimizing model performance through experimentation, hyperparameter tuning, and advanced optimization techniques is required. Proficiency in Python with the ability to get hands-on with coding at a deep level is a must. You will also be developing and maintaining APIs using Python's FastAPI, Flask, or Django for integrating AI capabilities into various systems. Having the ability to write optimized and high-performing scripts on relational databases (e.g., MySQL, PostgreSQL) or non-relational databases (e.g., MongoDB or Cassandra) is necessary. Enthusiasm for continuous learning and professional development in AI and related technologies is highly valued. Collaboration with cross-functional teams to understand business requirements and translate them into technical solutions is expected. Knowledge of cloud services like AWS, Google Cloud, or Azure is necessary. Proficiency with version control systems, especially Git, is required. Familiarity with data pre-processing techniques and pipeline development for AI model training is essential. Experience with deploying models using Docker, Kubernetes, AWS Bedrock, and Sagemaker is a plus. Strong problem-solving skills with the ability to translate complex business problems into AI solutions are crucial for success in this role.,

As a part of Cowbell's innovative team in the field of cyber insurance, you will play a crucial role in designing and implementing RAG-based systems, integrating LLMs with vector databases, search pipelines, and knowledge retrieval frameworks. Your responsibilities will include developing intelligent AI agents that automate tasks, retrieve relevant information, and enhance user interactions. You will work with APIs, embeddings, and multi-modal retrieval techniques to improve the performance of AI applications. Additionally, you will be tasked with optimizing inference pipelines and enhancing LLM serving, fine-tuning, and distillation for efficiency. Staying abreast of the latest advancements in generative AI and retrieval techniques will be essential, along with collaborating with stakeholders and cross-functional teams to address business needs and develop impactful ML models and AI-driven automation solutions. The ideal candidate for this position should hold a Master's degree in Computer Science, Data Science, AI, Machine Learning, or a related field (or a Bachelor's degree with significant experience). You should have at least 5 years of experience in machine learning, deep learning, and NLP for real-world applications, as well as a minimum of 1 year of hands-on experience with LLMs and generative AI. Expertise in RAG architectures, vector search, and retrieval methods is required, along with proficiency in Python and experience with LLM APIs such as OpenAI, Hugging Face, Anthropic, etc. Experience in integrating LLMs into real-world applications, solid foundation in machine learning, statistical modeling, and AI-driven software development, as well as knowledge of prompt engineering, few-shot learning, and prompt chaining techniques are also preferred qualifications. Strong software engineering skills, including experience with cloud platforms like AWS, and excellent problem-solving abilities, communication skills, and the capacity to work independently are crucial for this role. Preferred qualifications for the position include proficiency in PyTorch or TensorFlow for deep learning model development, experience in LLM fine-tuning, model compression, and optimization, familiarity with frameworks like LangChain, LlamaIndex, or Ollama, experience with multi-modal retrieval systems (text, image, structured data), and contributions to open-source AI projects or published research in AI/ML. At Cowbell, employees are offered an equity plan, wealth enablement plan for select customer-facing roles, comprehensive wellness program, meditation app subscriptions, lunch and learn sessions, a book club, happy hours, and more for professional development and growth opportunities. The company is committed to fostering a collaborative and dynamic work environment where transparency and resilience are valued, and every employee is encouraged to contribute and thrive. Cowbell is an equal opportunity employer, promoting diversity and inclusivity in the workplace and providing competitive compensation, comprehensive benefits, and continuous opportunities for professional development. To learn more about Cowbell and its mission in the cyber insurance industry, please visit https://cowbell.insure/.,

If you are a smart, self-motivated Machine Learning Scientist with a passion for advancing the field of Generative AI, an excellent opportunity awaits you. EXL, a rapidly expanding global digital data-led AI transformation solutions company, is seeking candidates with deep expertise in developing and fine-tuning Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), agentic solutions, and knowledge graph technologies to drive innovative solutions in Generative AI. You will have the chance to be at the forefront of pioneering advancements in AI, working alongside bright minds in an exciting R&D environment to build cutting-edge capabilities that redefine the future of artificial intelligence. In this role, you will develop initiatives in the Generative AI domain, focusing on cutting-edge technologies like LLMs, RAG, and autonomous agents. You will design and implement advanced workflows for integrating LLMs into real-world applications across domains such as Finance, Insurance, and Healthcare. Additionally, you will drive the development of retrieval-augmented systems by combining LLMs with document retrieval, clustering, and search techniques. Keeping abreast of AI advancements is essential, as you will be required to read, adapt, and implement cutting-edge research to solve real-world challenges. Documenting research findings, methodologies, and implementations for internal and external stakeholders will also be part of your responsibilities. Qualifications: - Experience: 2-5 years in AI/ML research and development, with at least 1-2 years focusing on Generative AI, LLMs, or related fields. - Education: Masters or PhD in Computer Science, AI, or a related field from a top-tier institution is highly preferred. Required Skills: - Core Expertise: Proven experience with Large Language Models (e.g., GPT-4, BERT, LLaMA, PaLM) and fine-tuning them for domain-specific applications. In-depth knowledge of Retrieval-Augmented Generation workflows and hands-on experience with autonomous agents. - Tools & Frameworks: Proficiency in deep learning frameworks like TensorFlow, PyTorch, or Hugging Face Transformers. Experience with distributed training and optimization on GPUs and TPUs. Familiarity with cloud ecosystems (AWS, Azure, Google Cloud) practices for scalable deployment. - Research & Development: Ability to read and adapt cutting-edge research papers for applied solutions in LLMs and knowledge graphs. Expertise in domain adaptation, few-shot learning, and zero-shot reasoning. Strong understanding of generative models and their integration with LLMs. - Problem Solving: Demonstrated ability to address challenges in unstructured data processing, including NLP and multimodal scenarios. Experience with document retrieval, clustering, and unsupervised learning techniques. Preferred Skills: - Experience with LLM fine-tuning and building Agentic systems for domain LLMs. Experience with reinforcement learning and fine-tuning via RLHF. Knowledge of large-scale optimization methods and efficient model compression techniques. Strong collaboration and communication skills, with a proven ability to lead teams. Experience with MoE based architecture and knowledge of federated learning.,

As an AIML - LLM Gen AI Engineer, your primary responsibility will be to design, develop, and implement solutions using transformer-based models for various Natural Language Processing (NLP) tasks. This includes tasks such as text generation, summarization, question answering, classification, and translation. You will be working extensively with transformer models like GPT, BERT, T5, RoBERTa, and similar architectures. Your deep understanding of model architectures, attention mechanisms, and self-attention layers will be crucial in effectively utilizing Large Language Models (LLMs) to generate human-like text. You will lead efforts in fine-tuning pre-trained LLMs and other transformer models on domain-specific datasets to optimize performance for specialized NLP tasks. In your role, you will apply your knowledge of attention mechanisms, context windows, tokenization, and embedding layers in model development and optimization. It will be essential to address and mitigate issues related to biases, hallucinations, and knowledge cutoffs that can impact LLM performance and output quality. You will craft clear, concise, and contextually relevant prompts to guide LLMs towards generating desired outputs, including the use of instruction-based prompting. Additionally, you will implement and experiment with zero-shot, few-shot, and many-shot learning techniques to maximize model performance without extensive retraining. Your iterative approach to prompt engineering strategies will involve refining outputs, rigorously testing model performance, and ensuring consistent and high-quality results. You will also be responsible for creating prompt templates for repetitive tasks that are adaptable to different contexts and inputs. Expertise in chain-of-thought (CoT) prompting will enable you to guide LLMs through complex reasoning tasks by encouraging step-by-step breakdowns. Your contribution will span the entire lifecycle of machine learning models in an NLP context, including training, fine-tuning, and deployment. **Required Skills & Qualifications:** - A minimum of 8 years of experience working with transformer-based models and NLP tasks, focusing on text generation, summarization, question answering, classification, and similar applications. - Proficiency in transformer models such as GPT, BERT, T5, RoBERTa, and foundational models. - Strong familiarity with model architectures, attention mechanisms, and self-attention layers for generating human-like text. - Proven experience in fine-tuning pre-trained models on domain-specific datasets for various NLP tasks. - Knowledge of attention mechanisms, context windows, tokenization, and embedding layers. - Awareness of biases, hallucinations, and knowledge cutoffs affecting LLM performance. - Ability to craft clear, concise, and contextually relevant prompts for LLMs. - Experience in zero-shot, few-shot, and many-shot learning techniques. - Proficiency in Python and NLP libraries like Hugging Face Transformers, SpaCy, NLTK. - Solid experience in training, fine-tuning, and deploying ML models in an NLP context. - Strong problem-solving skills and analytical mindset. - Excellent communication and collaboration abilities for remote or hybrid work environments. - Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, or related quantitative field. This role requires a combination of technical expertise in AI/ML, NLP, and transformer models, along with strong problem-solving and communication skills to achieve effective results in the field.,

As a Prompt Engineer in our Artificial Intelligence/Product Development/Data Science department, you will utilize your creativity, strong logical thinking, and expertise in large language models (LLMs) to assist in the establishment of an efficient and precise prompt system. Your role will involve driving the implementation of generative AI technology in our products, collaborating closely with machine learning engineers, product managers, designers, and researchers to enhance model response quality and elevate user interaction experiences. Your responsibilities will include designing, testing, and optimizing prompt templates for prominent LLMs like GPT-4, Claude, and Gemini. You will analyze model output, identify potential issues, and iterate on optimizations. Developing prompt libraries, establishing version control mechanisms, and integrating prompt designs into specific application scenarios are crucial aspects of the role. Staying updated on cutting-edge prompt engineering technologies and industry best practices is essential, as well as contributing to prompt data generation for fine-tuning and reinforcement learning. Furthermore, you will be instrumental in crafting prompt test evaluation processes, creating technical documents, and sharing prompt design experiences across teams. The ideal candidate should possess familiarity with mainstream LLMs, a solid foundation in natural language processing or linguistics, and practical experience in prompt engineering and few-shot learning. Proficiency in a programming language, exceptional logical reasoning and writing skills, and a good command of English are required. Bonus points will be awarded for experience in building AI assistants, familiarity with relevant tools, optimization expertise, and knowledge of model governance strategies. In return, we offer opportunities to work on cutting-edge generative AI products, a collaborative interdisciplinary environment that encourages exploration and experimentation, a flexible office system, competitive compensation, growth opportunities, and access to extensive real data, robust model resources, and GPU computing capabilities.,

As a Prompt Engineer in the Artificial Intelligence/R&D department, you will play a vital role in building, optimizing, and testing natural language prompts to enhance the performance of large language models like OpenAI GPT, Claude, and Gemini. Your primary responsibilities will involve designing, writing, and optimizing natural language prompts to address specific business or product challenges. Working alongside data scientists, product managers, and engineering teams, you will contribute to the development of high-quality AI prompt systems for real-world applications. Your role will require you to analyze and test the impact of different prompts on model output quality, collaborating closely with product and development teams to create deployable prompt templates and interface logic. Additionally, you will be involved in establishing prompt libraries and prompt engineering frameworks to enhance prompt reusability and scalability. Staying updated on the latest prompt engineering technologies and research papers will be crucial, as you transform these insights into practical applications. To excel in this role, you must demonstrate proficiency in utilizing mainstream large-scale language models such as GPT-4, Claude, or Gemini. Strong language expression and logical thinking skills, along with expertise in prompt tuning, few-shot learning, and chain-of-thought prompt engineering methods, are essential. A bachelor's degree in a relevant field (computer science, linguistics, cognitive science) is required, with a preference for candidates with strong English reading abilities to understand model documents and research papers. Candidates with experience in actual Large Language Model (LLM) application projects, mastery of Python programming for rapid prototyping, and familiarity with dialogue systems, search engines, knowledge graphs, and RAG technologies will be given bonus points. Understanding model fine-tuning, LoRA, and system prompt structure, as well as familiarity with tool chains like LangChain, LlamaIndex, and OpenAI Function Calling, will be advantageous. In return, we offer a competitive salary and bonus system, the chance to work with cutting-edge AI technology, flexible working hours, and remote work options. Join our team of technology enthusiasts and innovators to embark on a rewarding career journey.,

As a Senior QA Engineer at our company, you will be leading quality assurance for Generative AI (GenAI) solutions within our Digital Twin platform. Your role will involve focusing on the evaluation, reliability, and guardrails of AI-powered systems in production, going beyond traditional QA practices. Your responsibilities will include designing and implementing end-to-end QA strategies for applications using Node.js integrated with LLMs, RAG, and Agentic AI workflows. You will establish benchmarks and quality metrics for GenAI components, develop evaluation datasheets for LLM behavior validation, and conduct data quality testing for RAG databases. Additionally, you will perform A/B testing, define testing methodologies, collaborate with developers and AI engineers, build QA automation, and lead internal capability development by mentoring QA peers on GenAI testing practices. To be successful in this role, you should have at least 6 years of experience in software quality assurance, with a minimum of 3 years of experience in GenAI or LLM-based systems. You should possess a deep understanding of GenAI quality dimensions, experience in creating and maintaining LLM evaluation datasets, and familiarity with testing retrieval pipelines and RAG architectures. Preferred skills include experience with GenAI tools/platforms, exposure to evaluating LLMs in production settings, familiarity with prompt tuning and few-shot learning in LLMs, and basic scripting knowledge in Python, JavaScript, or TypeScript. If you are a passionate and forward-thinking QA Engineer with a structured QA discipline, hands-on experience in GenAI systems, and a strong sense of ownership, we encourage you to apply for this high-impact role within our innovative team.,