3 Llms Eg Jobs

As a highly skilled and innovative Automatic Speech Recognition (ASR) Senior Research Engineer at Cerence ai, you will have the opportunity to make a significant impact in the dynamic automotive industry alongside a high-performing global team. Cerence ai is the global leader in AI for transportation, specializing in AI and voice-powered companions for vehicles that enhance the overall driving experience for both drivers and passengers. With over 500 million cars equipped with Cerence ai technology, we collaborate with leading automakers and technology companies to create intuitive, integrated experiences that prioritize safety, connectivity, and enjoyment during journeys. Your role at Cerence ai will involve designing, building, and optimizing cutting-edge ASR models tailored for automotive applications, ensuring high accuracy and low latency in voice recognition tasks. You will be responsible for assessing the performance of ASR systems in real-world driving conditions, including diverse accents and languages, as well as noisy on-road environments. Collaboration with cross-functional teams, including product managers, software engineers, and UX designers, will be essential to seamlessly integrate ASR features into automotive voice assistant systems. Additionally, you will conduct innovative research in ASR technology, focusing on integrating generative AI and large language models to enhance voice interaction capabilities in automotive systems. To excel in this role, you should possess a Master's or Ph.D. in Computer Science & Engineering, Linguistics, Applied Mathematics, or a related field, along with 5+ years of hands-on experience in developing ASR systems, preferably for automotive applications. Proficiency in machine learning frameworks such as TensorFlow and PyTorch, as well as programming languages like Python, is required. Strong knowledge of speech processing technologies, natural language processing, deep learning techniques, and large language models is essential. Prior experience with cloud computing platforms and familiarity with automotive standards and protocols are advantageous. At Cerence ai, we offer a generous compensation and benefits package, including annual bonus opportunities, insurance coverage, paid time off, equity awards, and the option for remote or hybrid work arrangements. Join us in shaping the future of voice and AI in cars and be part of a dedicated team committed to driving innovation in the automotive industry. Cerence Inc. is an equal opportunity employer, and we value diversity, inclusion, and a collaborative work environment where employees can thrive and contribute to meaningful advancements in technology.,

As a NLP & Generative AI Engineer at Gigaforce, you will be part of our dynamic AI/ML team based in Noida, working on cutting-edge technologies to revolutionize the insurance claims processing industry. We are a California-based InsurTech company with a strong focus on innovation and digital transformation in the Property and Casualty sector. You must have a minimum of 2 years of hands-on experience in traditional machine learning, natural language processing, and modern generative AI techniques to be considered for this role. We are looking for individuals who are passionate about deploying GenAI solutions to production, working with open-source technologies, and handling document-centric pipelines efficiently. Your main responsibilities will include building and deploying NLP and GenAI-driven products focusing on document understanding, summarization, classification, and retrieval. You will be designing and implementing models using LLMs such as GPT, T5, and BERT, along with working on scalable, cloud-based pipelines for training, serving, and monitoring models. Collaboration is key at Gigaforce, and you will work closely with cross-functional teams including data scientists, ML engineers, product managers, and developers. Additionally, you will contribute to open-source tools and frameworks in the ML ecosystem, deploying production-ready solutions using MLOps practices, and working on distributed/cloud systems with GPU-accelerated workflows. To be successful in this role, you should have a strong grasp of traditional ML algorithms, NLP fundamentals, and modern NLP & GenAI models. Proficiency in Python, experience with cloud platforms like AWS SageMaker, GCP, or Azure ML, and familiarity with MLOps tools and distributed computing are essential. Experience with document processing pipelines and understanding insurance-related documents is a plus. If you are looking for a challenging role where you can lead the design, development, and deployment of innovative AI/ML solutions in the insurance industry, then this position at Gigaforce is the perfect opportunity for you. Join us in our mission to transform the claims lifecycle into an intelligent, end-to-end digital experience.,

We are looking for a highly motivated Mid-Level AI Engineer to join our growing AI team. Your main responsibility will be to develop intelligent applications using Python, Large Language Models (LLMs), and Retrieval-Augmented Generation (RAG) systems. Working closely with data scientists, backend engineers, and product teams, you will build and deploy AI-powered solutions that provide real-world value. Your key responsibilities will include designing, developing, and optimizing applications utilizing LLMs such as GPT, LLaMA, and Claude. You will also be tasked with implementing RAG pipelines to improve LLM performance using domain-specific knowledge bases and search tools. Developing and maintaining robust Python codebases for AI-driven solutions will be a crucial part of your role. Additionally, integrating vector databases like Pinecone, Weaviate, and FAISS, as well as embedding models for information retrieval, will be part of your daily tasks. You will work with APIs, frameworks like LangChain and Haystack, and various tools to create scalable AI workflows. Collaboration with product and design teams to define AI use cases and deliver impactful features will also be a significant aspect of your job. Conducting experiments to assess model performance, retrieval relevance, and system latency will be essential for continuous improvement. Staying up-to-date with the latest research and advancements in LLMs, RAG, and AI infrastructure is crucial for this role. To be successful in this position, you should have at least 3-5 years of experience in software engineering or AI/ML engineering, with a strong proficiency in Python. Experience working with LLMs such as OpenAI and Hugging Face Transformers is required, along with hands-on experience in RAG architecture and vector-based retrieval techniques. Familiarity with embedding models like SentenceTransformers and OpenAI embeddings is also necessary. Knowledge of API design, deployment, performance optimization, version control (e.g., Git), containerization (e.g., Docker), and cloud platforms (e.g., AWS, GCP, Azure) is expected. Preferred qualifications include experience with LangChain, Haystack, or similar LLM orchestration frameworks. Understanding NLP evaluation metrics, prompt engineering best practices, knowledge graphs, semantic search, and document parsing pipelines will be beneficial. Experience deploying models in production, monitoring system performance, and contributing to open-source AI/ML projects are considered advantageous for this role.,