Automation Testing With AI technologies SWITS DIGITAL Private Limited

6.0 - 10.0 years

0 Lacs

ahmedabad, gujarat

On-site

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.,

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ML Engineer Sportism

5.0 - 10.0 years

20 - 22 Lacs

Bengaluru

Work from Office

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.

Posted 2 months ago

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