Senior AI Full Stack Engineer (W2 Position)

Hi We have a job opportunity of a Role Senior AI Full Stack Engineer with given job description on W2. Please forward updated profile to or 1 .

Role: Senior AI Full Stack Engineer (W2 Position)

Location: Dearborn MI (Hybrid)

Duration: 12 Months

Experience: 7 Years

JD:

Skills Required:

• Full Stack Java

Skills Preferred:

• API Big Data Data Analysis Data Acquisition

Experience Required:

• Engineer 3 Exp: 2 coding lang. or adv. 1 lang. 6 years in IT; 4 years in development

Experience Preferred:

• We are looking for a Senior Full Stack Developer to build and scale AI-powered platforms that accelerate our vehicle development lifecycle.
• In the Product Development (PD) space your work will directly impact how we design engineer and validate the next generation of vehicles.
• You will be responsible for taking AI prototypes-ranging from generative design tools to automated engineering documentation assistants-and turning them into robust high-performance software products used by our global engineering teams.

Key Responsibilities:

• Engage with customers to deeply understand their use-cases pain points and requirements showcasing empathy and advocating for user-centric software solutions
• Solve complex problems by designing developing and delivering using various tools languages frameworks methodologies (like agile) and technologies
• Assess the requirements of the software application or service and determine the most suitable technology stack integration method deployment strategy etc.
• Create high-level software architecture designs that outline the overall structure components and interfaces of the application
• Collaborate with cross functional teams like product owners designers architects etc.
• Define and implement software test strategy guidelines policies and processes in line with organization vision industry regulations and market best practices.
• Work on continuously improving performance and optimizing the application and implement new technologies to maximize development efficiency
• Familiar with programming practices such as test-driven development continuous integration and continuous delivery
• Support security practices to safeguard user data including encryption and anonymization. 10. Create user-friendly and interactive interfaces
• Develop and maintain back-end applications like APIs and microservices using server-side languages
• Evaluate and incorporate emerging technologies and capabilities to deliver their solutions and are monitoring and participating in solutions for new stack layers often involving industry collaboration

Additional Information :

Hybrid. Mondays thru Thursdays onsite. Fridays remote.

• Engineering Tooling: Design and develop full-stack applications that integrate AI into the PD workflow (e.g. tools for CAD optimization automated requirement analysis or simulation data synthesis).
• AI-Driven R&D: Build Retrieval-Augmented Generation (RAG) systems that allow engineers to query decades of technical standards testing logs and past vehicle program data.
• Data Visualization & Digital Twins: Create sophisticated front-end interfaces to visualize complex engineering data 3D models or simulation results in real-time.
• Lifecycle Transition: Take proof-of-concept AI models developed by R&D teams and architect them into scalable production-ready internal products.
• API & Integration: Develop high-throughput APIs to connect AI services with existing PLM (Product Lifecycle Management) and ERP systems.
• Cloud platform : Excellent understanding of cloud architecture cloud services IAM CI/CD Security measures and Cloud networking concepts.
• Frontend: Expert in React or TypeScript.
• Backend: Deep proficiency in Python (FastAPI/Django).
• AI Orchestration: Advanced experience with LangChain LlamaIndex or Semantic Kernel. Proven ability to move beyond basic prompting into complex agentic workflows.
• Data Architecture: Expert knowledge of SQL (PostgreSQL) and Vector Databases. Experience with data lakes is a major advantage.
• Infrastructure: Strong experience with GCP cloud architectures.
• DevOps: Proficiency in Docker Kubernetes and building CI/CD pipelines that include model evaluation steps.
• Domain Expertise: Familiarity with the Product Development Lifecycle (Design CAE Testing Launch). Experience in automotive aerospace or heavy manufacturing is a plus.
• Analytical Mindset: Ability to understand complex engineering requirements and translate them into intuitive software features.