8 Nlp Libraries Jobs

About Astro 247: Astro 247 is an AI-powered astrology app that provides users with instant insights into their future. Our mission is to blend technology with ancient wisdom, offering a unique and engaging astrology experience. We are looking for a passionate AI Engineer Intern to join our growing team. This role is ideal for students or fresh graduates who are eager to apply their knowledge of AI/ML in a fast-paced, product-driven environment. This is a full-time, in-office internship based in Bangalore . Remote applicants will not be considered Responsibilities Research, design, and prototype AI/ML models for natural language understanding, recommendation systems, and conversational AI. Work on LLM fine-tuning, embeddings, prompt optimization, and dialogue design for astrology-based use cases. Collaborate with product and engineering teams to deploy scalable AI features into production. Analyze user interactions and feedback to continuously improve model accuracy and performance. Explore new techniques in Generative AI, text classification, and personalization to enhance the user experience. Requirements Pursuing or recently completed Bachelors/Masters in Computer Science, AI, Data Science, or related field. Strong foundation in Python, machine learning frameworks (PyTorch/TensorFlow), and NLP libraries (Transformers, LangChain, OpenAI API, etc.). Familiarity with LLMs, embeddings, vector databases, and conversational AI systems. Good problem-solving skills and ability to work independently in a startup environment. Bonus: Experience with MLOps tools, cloud platforms (AWS/GCP), or prior projects in chatbots/GenAI. What Youll Gain Competitive stipend for the internship Hands-on experience building and deploying real-world AI systems at scale. Mentorship from senior engineers and product leaders. Exposure to cutting-edge LLM, GenAI, and personalization techniques . Potential to transition into a full-time AI Engineer role after internship. Please send us your resume or portfolio/GitHub, and a short note to [HIDDEN TEXT] Show more Show less

Description We are seeking a talented NLP Engineer to join our team in India. The ideal candidate will have a solid background in Natural Language Processing and machine learning, with experience in developing innovative solutions that leverage NLP techniques. Responsibilities Develop and implement NLP models and algorithms for various applications. Collaborate with cross-functional teams to gather and understand data requirements. Perform data preprocessing, feature extraction, and model evaluation. Optimize and fine-tune NLP models for better performance and accuracy. Stay updated with the latest advancements in NLP and machine learning technologies. Skills and Qualifications 4-8 years of experience in Natural Language Processing and Machine Learning. Proficient in programming languages such as Python and Java. Strong understanding of NLP libraries and frameworks such as NLTK, spaCy, TensorFlow, or PyTorch. Experience with deep learning techniques and models such as LSTM, GRU, or Transformers. Familiarity with data preprocessing techniques, text mining, and semantic analysis. Knowledge of cloud platforms like AWS or Azure for deploying NLP solutions. Strong problem-solving skills and the ability to work independently or in a team.

Automation Anywhere is a leader in AI-powered process automation, utilizing AI technologies to drive productivity and innovation across organizations. The company's Automation Success Platform offers a comprehensive suite of solutions including process discovery, RPA, end-to-end process orchestration, document processing, and analytics, all with a security and governance-first approach. By empowering organizations globally, Automation Anywhere aims to unleash productivity gains, drive innovation, enhance customer service, and accelerate business growth. Guided by the vision to enable the future of work through AI-powered automation, the company is committed to unleashing human potential. Learn more at www.automationanywhere.com. Qualifications: - Bachelor's or Master's degree in Computer Science, Engineering, or a related field. - 6 to 12 years of relevant experience. - Proven track record as a Solution Architect or Lead, focusing on integrating Generative AI or exposure to Machine Learning. - Expertise in at least one RPA tool such as Automation Anywhere, UiPath, Blue Prism, Power Automate, and proficiency in programming languages like Python or Java. Skills: - Proficiency in Python or Java for programming and architecture. - Strong analytical and problem-solving skills to translate business requirements into technical solutions. - Experience with statistical packages and machine learning libraries (e.g., R, Python scikit-learn, Spark MLlib). - Familiarity with RDBMS, NoSQL, and Cloud Platforms like AWS/AZURE/GCP. - Knowledge of ethical considerations and data privacy principles related to Generative AI for responsible integration within RPA solutions. - Experience in process analysis, technical documentation, and workflow diagramming. - Designing and implementing scalable, optimized, and secure automation solutions for enterprise-level AI applications. - Expertise in Generative AI technologies such as RAG, LLM, and AI Agent. - Advanced Python programming skills with specialization in Deep Learning frameworks, ML libraries, NLP libraries, and LLM frameworks. Responsibilities: - Lead the design and architecture of complex RPA solutions incorporating Generative AI technologies. - Collaborate with stakeholders to align automation strategies with organizational goals. - Develop high-level and detailed solution designs meeting scalability, reliability, and security standards. - Take technical ownership of end-to-end engagements and mentor a team of senior developers. - Assess the applicability of Generative AI algorithms to optimize automation outcomes. - Stay updated on emerging technologies, particularly in Generative AI, to evaluate their impact on RPA strategies. - Demonstrate adaptability, flexibility, and willingness to work from client locations or office environments as needed. Kindly note that all unsolicited resumes submitted to any @automationanywhere.com email address will not be eligible for an agency fee.,

As a Prompt Engineering Specialist, you will be responsible for the design, development, implementation, and maintenance of prompt systems within our software solutions. Your role is crucial in ensuring that our prompt-based systems are user-friendly, intuitive, and responsive, meeting the highest standards of quality and performance. Your responsibilities will include developing, refining, and optimizing prompts for various AI language model applications like text generation, translation, and question answering. Collaboration with engineers, scientists, and product managers is essential to understand user needs and create prompts that align with those requirements. Evaluating prompt performance, making necessary adjustments to enhance accuracy, informativeness, and engagement, and staying updated on the latest AI and NLP research advancements to improve prompt generation capabilities are key aspects of your role. Additionally, documenting and sharing research findings, methodologies, and technical specifications with team members and stakeholders is crucial. To qualify for this position, you should hold a Bachelor's or Master's degree in Computer Science, Artificial Intelligence, or a related field. You must have at least 1 year of experience in AI language models and prompt engineering, along with strong programming skills in Python, proficiency in data structures and algorithms, and a solid understanding of NLP fundamentals such as tokenization, text classification, sequence tagging, and language modeling. Experience with NLP libraries and pre-trained models (e.g., NLTK, Transformers), familiarity with data preprocessing techniques and feature engineering, as well as a good grasp of AWS, Serverless, and Linux, are required. Excellent communication, interpersonal skills, the ability to work independently and collaboratively are essential for success in this role. This opportunity allows you to work on cutting-edge projects, leverage the latest research, and contribute to the development of innovative solutions. If you are passionate about natural language processing and eager to make a significant impact, we invite you to apply for this full-time position with paid time off, day shift schedule, and performance bonus. Location: Mohali, Punjab. Relocation or reliable commuting is required. Education: Bachelor's degree is required. Experience: Minimum of 1 year of relevant work experience is required.,

Description We are seeking an experienced NLP Engineer to join our team in India. The ideal candidate will have a strong background in natural language processing and machine learning, with the ability to develop and implement innovative solutions to complex language challenges. Responsibilities Design and implement natural language processing (NLP) models and algorithms. Collaborate with data scientists and software engineers to integrate NLP capabilities into existing applications. Conduct research to improve existing NLP models and explore new approaches to solving language-related problems. Analyze and preprocess large datasets for training NLP models. Deploy and maintain NLP models in production environments, ensuring high performance and scalability. Skills and Qualifications 5-10 years of experience in natural language processing or related fields. Strong programming skills in Python, Java, or similar languages. Proficiency in NLP libraries and frameworks such as NLTK, SpaCy, TensorFlow, or PyTorch. Experience with machine learning algorithms and statistical methods. Familiarity with data preprocessing techniques for text data. Knowledge of deep learning architectures for NLP, including LSTM, CNN, and Transformers. Strong analytical and problem-solving skills. Excellent communication and teamwork abilities.

Description We are seeking a skilled NLP Engineer to join our team in India. The ideal candidate will have a strong background in natural language processing and machine learning, with a passion for developing innovative solutions that leverage language data. Responsibilities Develop and implement natural language processing (NLP) models and algorithms to solve real-world problems. Collaborate with data scientists and software engineers to integrate NLP capabilities into applications. Analyze and preprocess large datasets to improve model performance. Conduct research on the latest NLP techniques and frameworks to enhance existing systems. Optimize and fine-tune NLP models for better accuracy and efficiency. Skills and Qualifications 3-8 years of experience in Natural Language Processing or related field. Proficiency in programming languages such as Python, Java, or Scala. Strong understanding of NLP libraries and frameworks like NLTK, SpaCy, TensorFlow, or PyTorch. Experience with machine learning techniques and algorithms relevant to NLP. Familiarity with text preprocessing, feature extraction, and model evaluation metrics. Knowledge of deep learning architectures for NLP, including LSTM, Transformers, and BERT. Ability to work with large datasets and experience with data manipulation tools. Strong problem-solving skills and the ability to work in a collaborative environment.

Position Overview : We are seeking an experienced NLP & LLM Specialist to join our team. The ideal candidate will have deep expertise in working with transformer-based models, including GPT, BERT, T5, RoBERTa, and similar models. This role requires experience in fine-tuning these pre-trained models on domain-specific tasks, as well as crafting and optimizing prompts for natural language processing tasks such as text generation, summarization, question answering, classification, and translation. The candidate should be proficient in Python and familiar with NLP libraries like Hugging Face, SpaCy, and NLTK, with a solid understanding of model evaluation metrics. Roles and Responsibilities : - Model Expertise : Work with transformer models such as GPT, BERT, T5, RoBERTa, and others for a variety of NLP tasks, including text generation, summarization, classification, and translation. - Model Fine-Tuning : Fine-tune pre-trained models on domain-specific datasets to improve performance for specific applications such as summarization, text generation, and question answering. - Prompt Engineering : Craft clear, concise, and contextually relevant prompts to guide transformer-based models towards generating desired outputs for specific tasks. - Iterate on prompts to optimize model performance. - Instruction-Based Prompting : Implement instruction-based prompting to guide the model toward achieving specific goals, ensuring that the outputs are contextually accurate and aligned with task objectives. - Zero-shot, Few-shot, Many-shot Learning : Utilize zero-shot, few-shot, and many-shot learning techniques to improve model performance without the need for full retraining. - Chain-of-Thought (CoT) Prompting : Implement Chain-of-Thought (CoT) prompting to guide models through complex reasoning tasks, ensuring that the outputs are logically structured and provide step-by-step explanations. - Model Evaluation : Use evaluation metrics such as BLEU, ROUGE, and other relevant metrics to assess and improve the performance of models for various NLP tasks. - Model Deployment : Support the deployment of trained models into production environments and integrate them into existing systems for real-time applications. - Bias Awareness : Be aware of and mitigate issues related to bias, hallucinations, and knowledge cutoffs in LLMs, ensuring high-quality and reliable outputs. - Collaboration : Collaborate with cross-functional teams including engineers, data scientists, and product managers to deliver efficient and scalable NLP solutions. Must Have Skill : - Overall 7 years with at least 5+ years of experience working with transformer-based models and NLP tasks, with a focus on text generation, summarization, question answering, classification, and similar tasks. - Expertise in transformer models like GPT (Generative Pre-trained Transformer), BERT (Bidirectional Encoder Representations from Transformers), T5 (Text-to-Text Transfer Transformer), RoBERTa, and similar models. - Familiarity with model architectures, attention mechanisms, and self-attention layers that enable LLMs to generate human-like text. - Experience in fine-tuning pre-trained models on domain-specific datasets for tasks such as text generation, summarization, question answering, classification, and translation. - Familiarity with concepts like attention mechanisms, context windows, tokenization, and embedding layers. - Awareness of biases, hallucinations, and knowledge cutoffs that can affect LLM performance and output quality. - Expertise in crafting clear, concise, and contextually relevant prompts to guide LLMs towards generating desired outputs. - Experience in instruction-based prompting. - Use of zero-shot, few-shot, and many-shot learning techniques for maximizing model performance without retraining. - Experience in iterating on prompts to refine outputs, test model performance, and ensure consistent results. - Crafting prompt templates for repetitive tasks, ensuring prompts are adaptable to different contexts and inputs. - Expertise in chain-of-thought (CoT) prompting to guide LLMs through complex reasoning tasks by encouraging step-by-step breakdowns. - Proficiency in Python and experience with NLP libraries (e.g., Hugging Face, SpaCy, NLTK). - Experience with transformer-based models (e.g., GPT, BERT, T5) for text generation tasks. - Experience in training, fine-tuning, and deploying machine learning models in an NLP context. - Understanding of model evaluation metrics (e.g., BLEU, ROUGE). Qualification : - BE/B.Tech or Equivalent degree in Computer Science or related field. - Excellent communication skills in English, both verbal and written.

Role & responsibilities Overall 7 years with at least 5+ years of experience working with transformer-based models and NLP tasks, with a focus on text generation, summarization, question answering, classification, and similar tasks. Expertise in transformer models like GPT (Generative Pre-trained Transformer), BERT (Bidirectional Encoder Representations from Transformers), T5 (Text-to-Text Transfer Transformer), RoBERTa, and similar models. Familiarity with model architectures, attention mechanisms, and self-attention layers that enable LLMs to generate human-like text. Experience in fine-tuning pre-trained models on domain-specific datasets for tasks such as text generation, summarization, question answering, classification, and translation. Familiarity with concepts like attention mechanisms, context windows, tokenization, and embedding layers. Awareness of biases, hallucinations, and knowledge cutoffs that can affect LLM performance and output quality. Expertise in crafting clear, concise, and contextually relevant prompts to guide LLMs towards generating desired outputs. Experience in instruction-based prompting Use of zero-shot, few-shot, and many-shot learning techniques for maximizing model performance without retraining. Experience in iterating on prompts to refine outputs, test model performance, and ensure consistent results. Crafting prompt templates for repetitive tasks, ensuring prompts are adaptable to different contexts and inputs. Expertise in chain-of-thought (CoT) prompting to guide LLMs through complex reasoning tasks by encouraging step-by-step breakdowns. Proficiency in Python and experience with NLP libraries (e.g., Hugging Face, SpaCy, NLTK). Experience with transformer-based models (e.g., GPT, BERT, T5) for text generation tasks. Experience in training, fine-tuning, and deploying machine learning models in an NLP context. Understanding of model evaluation metrics (e.g., BLEU, ROUGE)