Lead AI Solution Engineer
1 week ago
Job Description:
The Impact You'll Make in this Role
As a Lead AI Solution Engineer, you will be a strategic and technical driver of 3M's enterprise AI initiatives, guiding cross-functional teams to design, build, and scale cutting-edge AI solutions. Your role goes beyond engineering—you will set the vision, mentor teams, ensure governance, and deliver high-value AI outcomes aligned with business goals.
Here, you will make an impact by:
Key Responsibilities:
- Leading AI Solution Development: Drive the design, development, and deployment of AI/ML and Generative AI solutions, ensuring scalability, security, and performance across global environments.
- Establishing Best Practices: Define standards for LLMOps/MLOps, model lifecycle management, governance, and compliance while embedding responsible AI principles.
- Building High-Performance Teams: Lead, mentor, and upskill engineers, data scientists, and architects; foster an innovation-driven culture with measurable impact.
- Driving Generative AI Adoption: Develop and deploy agentic AI systems, RAG pipelines, vector databases, and AI orchestration frameworks that integrate seamlessly into enterprise workflows.
- Owning Governance & Compliance: Implement robust security, privacy, and ethical AI practices; enforce policies for model explainability, drift monitoring, and lineage tracking.
- Innovating Continuously: Evaluate and adopt emerging AI technologies (LLMs, LangChain, Semantic Kernel, vector DBs) and run proof-of-concept initiatives to keep 3M ahead of the curve.
- Partnering Across the Enterprise: Act as the AI thought leader, collaborating with business stakeholders, cloud architects, and product teams to deliver transformative solutions.
To set you up for success in this role from day one, 3M requires (at a minimum) the following qualifications:
- Bachelor's degree or higher in Computer Science, Engineering, or related field (completed and verified prior to start).
- 9+ years of experience in AI/ML engineering, data science, or platform leadership roles, including 3+ years in a leadership capacity.
- Strong expertise in MLOps and LLMOps, with hands-on experience using tools like Docker, Kubernetes, Airflow, MLflow, LangChain, or Semantic Kernel.
- Deep experience in cloud ecosystems (AWS or Azure) and working knowledge of multi-cloud integration.
- Proven track record of delivering production-grade AI systems, including model deployment, CI/CD pipelines, and infrastructure automation (Terraform, Bicep, CloudFormation).
- Certified AWS Cloud Solutions Architect
Additional qualifications that could help you succeed even further in this role include:
- Master's degree in Computer Science, Engineering, or related field.
- Strong understanding of LLMs and Generative AI frameworks, vector databases (e.g., Pinecone, Weaviate, OpenSearch), and prompt engineering techniques.
- Experience in AI product development and scaling AI applications in enterprise environments.
- Excellent leadership, communication, and collaboration skills, with the ability to influence at all levels of the organization.
Please note: your application may not be considered if you do not provide your education and work history, either by: 1) uploading a resume, or 2) entering the information into the application fields directly.
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