3 Openais Gpt Jobs

The candidate for the Automation Testing With AI technologies role will be responsible for developing and executing automated test scripts using Python and the Robot Framework. You will integrate AI technologies to enhance QA processes, ensuring robust and efficient testing methodologies. Your expertise in AI tools and platforms like Copilot and Cursor will be crucial in optimizing testing strategies. You will be required to integrate AI technologies, including LLMs such as OpenAI's GPT (GPT-3, GPT-4), GitHub Copilot, Anthropic's Claude, and CodeGen into testing processes to enhance efficiency and accuracy. Utilizing LLMs to generate test cases, automate documentation, and assist in exploratory testing by predicting potential failure points will be part of your responsibilities. Furthermore, you will enhance test scripts and processes with AI capabilities to predict and prevent potential failures. It is essential to stay updated on emerging AI technologies relevant to testing (e.g., Copilot, Cursor) and provide guidance and support on the use of AI tools to improve testing practices. You will also be expected to develop and document automation strategies, tools, processes, and best practices using Python with the Robot Framework. This role requires 6-10 years of experience and can be based in Mumbai, Bangalore, Kolkata, Chennai, Pune, Gurgaon, or Ahmedabad.,

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.,

Role Overview We are seeking a skilled and innovative Machine Learning Engineer with expertise in Large Language Models (LLMs) to join our team. The ideal candidate has hands-on experience developing, fine-tuning, and deploying LLMs, alongside a deep understanding of the machine learning lifecycle. This role involves building scalable AI solutions, collaborating with cross-functional teams, and contributing to cutting-edge AI initiatives. Key Responsibilities Model Development & Optimization: Develop, fine-tune, and deploy LLMs like OpenAI's GPT, Anthropic's Claude, Googles Gemini, or AWS Bedrock. Customize pre-trained models for specific use cases, ensuring high performance and scalability. Machine Learning Pipeline Design: Build and maintain end-to-end ML pipelines, from data preprocessing to model deployment. Optimize training workflows for efficiency and accuracy. Integration & Deployment: Work closely with software engineering teams to integrate ML solutions into production environments. Ensure APIs and solutions are scalable and robust. Experimentation & Research: Experiment with new architectures, frameworks, and approaches to improve model performance. Stay updated with advancements in LLMs and generative AI technologies. Collaboration: Collaborate with cross-functional teams, including data scientists, engineers, and product managers, to align ML solutions with business goals. Provide mentorship to junior team members as needed. Required Qualifications Experience: At least 5 years of professional experience in machine learning or AI development. Proven experience with LLMs and generative AI technologies. Technical Skills: Proficiency in Python (required) and basic knowledge of Java is needed Hands-on experience with APIs and tools like OpenAI, Anthropic's Claude, Google Gemini, or AWS Bedrock. Familiarity with ML frameworks such as TensorFlow, PyTorch, or Hugging Face. Strong understanding of data structures, algorithms, and distributed systems. Cloud Expertise: Experience with AWS, GCP, or Azure, including services relevant to ML workloads (e.g., AWS SageMaker, Bedrock). Data Engineering: Proficiency in handling large-scale datasets and implementing data pipelines. Experience with ETL tools and platforms for efficient data preprocessing. Problem Solving: Strong analytical and problem-solving skills, with the ability to debug and resolve issues quickly. Preferred Qualifications Experience with multi-modal models and generative AI for images, text, or other modalities. Understanding of ML Ops principles and tools (e.g., MLflow, Kubeflow). Familiarity with reinforcement learning and its applications in AI. Knowledge of distributed training techniques and tools like Horovod or Ray. Advanced degree (Masters or Ph.D.) in Computer Science, Machine Learning, or a related field.