Senior GenAI Engineer Architect at Dallas, Atlanta, Chicago, SFO and Seattle

Job Description 

We are seeking a creative and innovative Senior GenAI Engineer Architect /Data Scientist to join our team. This role involves developing cutting-edge custom GEN AI solutions and predictive AI models, deploying them in production environments, and driving the integration of AI technologies across our business operations. As a key member of our AI team, you will collaborate with diverse teams to design solutions that deliver tangible business value through AI-driven insights 

Key Responsibilities 

Gen AI & Machine Learning Models Development 

• Design, develop, and implement both generative and predictive AI models (including NLP, computer vision, etc.). 

• Lead the development of innovative solutions such as intelligent autonomous agents for complex tasks and multimodal interactions. 

• Integrate agentic workflows that utilize AI agents to automate tasks and improve operational efficiency. 

• Work closely with cross-functional teams, including data scientists, software engineers, and product managers, to align AI solutions with business objectives.  

Application Architecture Design, Development, & Integration 

• Demonstrates proficiency in API architecture and design to develop custom generative AI applications built with external interfacing, traffic control, runtime execution of business logic, data access, authentication, and deployment components.  

• Work with cloud platforms (e.g., Azure, AWS, Google Cloud Platform) and big data tools (e.g., Databricks, PySpark) to develop AI solutions. 

• Optimize AI models and algorithms for performance, scalability, and efficiency.  

• Ensure the security and compliance of AI solutions, adhering to best practices and regulations. 

• Implement monitoring and logging mechanisms to track performance, reliability, and usage of AI solutions 

Model Deployment & Maintenance 

• Work closely with DevOps and IT teams to deploy AI models into production environments, ensuring scalability, performance, and optimization. 

• Monitor and troubleshoot deployed models and pipelines for optimal performance. 

• Design and maintain data pipelines for efficient data collection, processing, and storage (e.g., data lakes, data warehouses). 

• Create comprehensive documentation for deployment processes and provide training to relevant teams to ensure proper model management. 

AI Research & Integration 

• Conduct research to stay at the forefront of emerging AI techniques, tools, and trends. 

• Integrate new AI tools and methodologies into existing business processes and applications. 

• Lead the development of private GenAI tools, including LLMs and specialized AI use cases. 

Collaboration & Communication: 

• Collaborate with cross-functional teams to align AI projects with business requirements and strategic goals. 

• Lead and contribute to GenAI and AI business development efforts such as capability pitches, demos, proposals, and overall project planning. 

• Communicate complex AI concepts and results to non-technical stakeholders. 

• Lead and mentor a small team of junior engineers and data scientists. 

Required Skills 

• Demonstrated excellence in Agentic AI architecture, encompassing development, testing, and research of GenAI agents, utilizing current-generation deployments and pioneering next-generation patterns and research. 

• Skilled in constructing agentic systems through techniques such as multi-agent systems, reinforcement learning, dynamic workflows, caching/memory management, and concurrent orchestration.  

• Proficient in various AI and Agentic AI frameworks, including LangChain/LangGraph, Crew AI, Semantic Kernel or Open AI Agentic SDK. 

• Strong skills in prompt engineering and its techniques including design, development, and refinement of prompts (zero-shot, few-shot, and chain-of-thought approaches) to maximize accuracy and leverage optimization tools. 

• Proficiency in programming languages such as Python and SQL, as well as machine learning libraries like TensorFlow, PyTorch, and scikit-learn. 

• Proficiency in URLs and API Endpoints, HTTP Requests, Authentication Methods, Response Types, JSON/REST, Parameters and Data Filtering, Error Handling, Debugging, Rate Limits, Tokens, Integration, and Documentation. 

• Experience with cloud platforms (e.g., AWS, Azure) and big data tools (e.g., Databricks, PySpark). 

• Knowledge of deployment tools (e.g., Azure DevOps, Docker, AWS ECS/EKS/Fargate) and CI/CD pipelines (AWS CloudFormation, CodeDeploy). 

• Strong understanding of data engineering principles, including experience with SQL and NoSQL databases (e.g., MySQL, MongoDB, Redis).