Senior Applied AI Engineer

Responsibilities

Bringing the State of the Art to Products 

• Build collaborative relationships with product and business groups to deliver AI-driven impact 

• Research and implement state-of-the-art using foundation models, prompt engineering, RAG, graphs, multi-agent architectures, as well as classical machine learning techniques. 

• Fine-tune foundation models using domain-specific datasets. - Evaluate model behavior on relevance, bias, hallucination, and response quality via offline evaluations, shadow experiments, online experiments, and ROI analysis. 

• Build rapid AI solution prototypes, contribute to production deployment of these solutions, debug production code, support MLOps/AIOps. 
  Contribute to papers, patents, and conference presentations. - Translate research into production-ready solutions and measure their impact through A/B testing and telemetry that address customer needs.  

• Ability to use data to identify gaps in AI quality, uncover insights and implement PoCs to show proof of concepts. 

• Proven programming expertise (e.g., in Python or leveraging AI-first IDEs and SWE agents), with a strong record of building reliable, well-documented research code that drives rapid experimentation, scalable evaluation, and efficient deployment from prototype to production in applied AI research. 

Leveraging Research in real-world problems 

• Demonstrate deep expertise in AI subfields (e.g., deep learning, Generative AI, NLP, muti-modal models) to translate cutting-edge research into practical, real-world solutions that drive product innovation and business impact. 

• Share insights on industry trends and applied technologies with engineering and product teams. 

• Formulate strategic plans that integrate state-of-the-art research to meet business goals. 

Documentation 

• Maintain clear documentation of experiments, results, and methodologies. 

• Share findings through internal forums, newsletters, and demos to promote innovation and knowledge sharing  

Ethics, Privacy and Security 

Apply a deep understanding of fairness and bias in AI by proactively identifying and mitigating ethical and security risks—including XPIA (Cross-Prompt Injection Attack) unfairness, bias, and privacy concerns—to ensure equitable and responsible outcomes. 

• Ensure responsible AI practices throughout the development lifecycle, from data collection to deployment and monitoring. 

• Contribute to internal ethics and privacy policies and ensure responsible AI practice throughout AI development cycle from data collection to model development, deployment, and monitoring.  

Specialty Responsibilities 

• Design, develop, and integrate generative AI solutions using foundation models and more. 

• Deep understanding of small and large language models architecture, Deep learning, fine tuning techniques, multi-agent architectures, classical ML,  and optimization techniques to adapt out-of-the-box solutions to particular business problems 

• Prepare and analyze data for machine learning, identifying optimal features and addressing data gaps. 

• Develop, train, and evaluate machine learning models and algorithms to solve complex business problems, using modern frameworks and state-of-the-art models, open-source libraries, statistical tools, and rigorous metrics 

• Address scalability and performance issues using large-scale computing frameworks. 

• Monitor model behavior, , guide product monitoring and alerting, and adapt to changes in data streams. 

Qualifications

Required Qualifications 

• Bachelor’s degree in Computer Science, Statistics, Electrical/Computer Engineering, Physics, Mathematics or related field AND 4+ years of experience in AI/ML, predictive analytics, or research 
  - OR Master’s degree AND 3+ years of experience 
  - OR PhD AND 1+ year of experience 
  - OR equivalent experience 

• 1+ years of experience wi