**Title**:GenAI Engineer **Job Type**:Full-Time **Location**:Remote **Company Description**: **Job Summary**: **Key Responsibilities**: - Contribute to building and enhancing our Mechanized AI platform and AI-enabled - products including mAI Modernize - Serve as ML SME on client projects as needed - Design ML systems - Research and implement appropriate ML algorithms and tools - Select appropriate datasets and data representation methods - Run ML tests and experiments - Perform statistical analysis and fine-tuning using test results - Train and retrain systems when necessary - Extend existing ML libraries and frameworks - Stay current with emerging technologies and ML best practices to continuously - improve our methodologies and tools **Required Skills & Experience**: - 4+ years of ML experience at a start-up or larger enterprise - high priority - Client delivery experience - high priority - Effective written and oral communications skills (C1/C2 - advanced/proficient level English is required) - high priority - Bachelor's degree in computer science, software engineering or related field - Experience with cloud environments (e.g., AWS, Azure, GCP) - Experience with ML frameworks and libraries (TensorFlow, PyTorch, Keras, scikit-learn) - Experience developing, deploying, and managing/monitoring models - Knowledge of containerization technologies (e.g., Docker, Kubernetes) and microservices architecture - Expertise in Object-Oriented Programming (OOP) principles and unit test-driven development methodologies - Strong proficiency in Python programming - Familiarity with prompt engineering approaches and best practices - Knowledge of data structures, data modeling, and software architecture - Strong analytical and problem-solving skills, with ability to propose innovative solutions and troubleshoot issues - Ability to work independently and as part of a collaborative team in a fast-paced environment **Preferred Qualifications**: - Experience in any of the following: - Agent development - Data privacy - Fine tuning LLMs - LLM architecture and techniques for performance - MLOps - ML evaluation - Model decay and data drift detection and handling - Pulumi, Terraform, and/or Cloud SDKs - PySpark - Quantization - Retrieval-augmented generation (RAG) optimization - Security - Vector databases **Contact**: Gabriel Martinez Recruiter Mechanized AI