Artificial Intelligence Engineer

Job Title: AI/ML Engineer Location: Remote, USA Overview • We are seeking an innovative and results-oriented Mid-Level AI/ML Engineer to join our dynamic team. This role is crucial for transforming novel concepts into robust, production-ready AI solutions. The ideal candidate possesses a strong background in Machine Learning engineering, extensive experience with cutting-edge LLMs and cloud-based AI services, and a commitment to maintaining high-quality, responsible AI systems. Key Responsibilities • Full ML Lifecycle Management: Drive projects from initial ideation to production deployment, including data pipeline development, model training, validation, and serving. • LLM & Agentic Development: Design, implement, and optimize solutions utilizing Large Language Models (LLMs) and developing sophisticated Agentic AI systems to solve complex business problems. • Platform Expertise: Leverage and integrate core generative AI platforms, including Gemini and Amazon Bedrock, to build scalable and efficient solutions. • MLOps & Tools: Implement MLOps best practices, utilizing tools like MLFlow for experiment tracking, model versioning, and pipeline orchestration. • Quality Assurance: Develop and execute comprehensive testing strategies for LLM applications, including utilizing frameworks like DeepEval for prompt engineering and model output quality. • Analytical Skill: Apply strong analytical skills to evaluate model performance, diagnose issues, and iterate on solutions to achieve maximum business impact. • Collaboration: Work closely with cross-functional teams (data scientists, product managers, and software engineers) to define requirements and deliver integrated AI features. Required Qualifications • Experience: 4-7 years of professional experience in Machine Learning Engineering, AI Development, or a closely related field. Education: • Master’s degree in Computer Science, Data Science, Engineering, or a quantitative field. Technical Proficiency: • Expertise in Python and core ML/Data Science libraries (e.g., PyTorch, TensorFlow, Scikit-learn). • Proven experience in deploying models on major cloud platforms (GCP, AWS, or Azure). • Deep understanding of the architecture and fine-tuning of Large Language Models. • Domain Knowledge: Practical experience with MLOps tools (e.g., MLFlow) and validation frameworks (e.g., DeepEval). • Problem Solving: Demonstrated ability to apply analytical skills to complex, ambiguous problems and translate insights into actionable engineering solutions. Preferred Qualifications • Hands-on experience developing applications or services using Google's Gemini API or models. • Direct experience with AWS services related to AI/ML, particularly Amazon Bedrock. • Experience in building and managing multi-step, reasoning-based Agentic AI systems. • Prior experience in optimizing models for latency and cost efficiency in a production environment.