AI Engineering with Hardware testing

We are looking for an experienced AI Engineer with 7 to 8 years of experience in Generative AI, Python development, real-time data processing, and hardware testing. The ideal candidate should have expertise in developing AI-driven solutions, working with streaming data, ensuring data quality, and validating hardware systems. Key Responsibilities Design, develop, and deploy applications using multiple Generative AI models (LLMs, multimodal models, embedding models, etc.). Evaluate and integrate different GenAI models based on business use cases, performance, cost, and latency. Develop scalable data pipelines using Python for data processing, analytics, and AI model integration. Process and analyze live/streaming data to build real-time dashboards, visualizations, and alert mechanisms. Implement robust data quality validation processes to identify and resolve data inconsistencies. Perform hardware testing and validation, ensuring seamless integration between hardware devices and AI/software applications. Develop and execute hardware test plans, perform functional and performance testing, and troubleshoot hardware-related issues. Collaborate with hardware, firmware, software, and data engineering teams to validate end-to-end system functionality. Optimize AI applications and data pipelines for performance, scalability, and reliability. Required Skills 4–5 years of experience in AI/ML, data engineering, or software engineering. Strong understanding of Generative AI and experience with multiple LLMs (e.g., GPT, Claude, Gemini, Llama, Mistral). Strong proficiency in Python for data processing, analytics, and AI application development. Experience with streaming data technologies such as Kafka, Spark Streaming, Flink, or similar platforms. Experience building real-time dashboards and visualization solutions using tools such as Grafana, Power BI, Tableau, or Streamlit. Strong understanding of data quality validation, data profiling, anomaly detection, and data governance. Hands-on experience in hardware testing, system validation, debugging, and troubleshooting. Experience working with hardware interfaces, sensors, embedded systems, or IoT devices is an advantage. Good knowledge of SQL and modern data processing frameworks such as Pandas, PySpark, or Polars. Familiarity with cloud platforms (AWS, Azure, or Google Cloud). Experience with APIs, vector databases, RAG architectures, and AI agent frameworks is a plus. Preferred Qualifications Experience deploying AI solutions in production environments. Knowledge of MLOps/LLMOps practices. Strong analytical, problem-solving, and communication skills. Embedded project development and testing Communication Skills Excellent command of the English language (spoken and written) Impeccable communication skills, written, verbal, and formal presentations