
Subject Matter Expert
1 week ago
Department: Artificial Intelligence & Machine Learning
Location: Remote
We are looking for a highly experienced Subject Matter Expert (SME) with deep expertise in MLOps,
scalable ML engineering, AI infrastructure, and secure cloud deployments. This is a curriculum-focused,
impact-driven role for professionals who are not only technically accomplished, but also passionate about
teaching and empowering the next generation of AI/ML engineers.
As a core contributor to our AI education initiative, youll shape and review advanced technical courses
designed for mid-to-senior professionals building and deploying production-grade ML systems. Youll
design learning objectives, validate technical content, deliver on-camera sessions, and help us ensure
instructional excellence across our MLOps and AI infrastructure tracks.
Courses Covered:
ML Engineering & MLOps And AI Infrastructure & DevSecOps
TDD Python for ML
Optimize & Ship ML Code
GPU Clusters & Containers
Scale with Kubernetes
Streamline App Release Workflows
Enforce Cloud Security Guardrails
Optimize System Maintenance Ops
Deploy AI Data Entry
Design Secure Multimodal APIs.
Areas of Impact:
- Curriculum Development: Design modular, industry-aligned learning pathways in MLOps, ML
engineering, and AI infrastructureincluding beginner to advanced levels.
- Content Creation: Develop high-quality instructional content such as video scripts, technical
tutorials, slide decks, assessments, lab exercises, and capstone projects.
- Thought Leadership: Act as the public-facing expert via talking-head videos, interviews, webinars,
and other branded educational media.
- Mentorship & Support: Provide academic mentoring and technical guidance to learners through
live sessions, office hours, or discussion forums.
- Learning Outcome Design: Collaborate with instructional designers to define learning objectives,
success metrics, and skill progression frameworks.
- Quality Assurance: Review and enhance existing content for technical accuracy, instructional
quality, and real-world application.
- Industry Relevance: Ensure all learning materials reflect current trends in AI infrastructure, tools,
and workflows, including platforms such as Kubeflow, MLflow, and cloud-native ML stacks.
Scope Of Work:
Analyze/create learning objectives for each course.
Review/create Course Outline for each of the courses.
Review video scripts (7-9 per course) and confirm technical accuracy of the content,
suggest edits and updates as required. Incorporate one round of internal and client
feedback.
- Provide relevant static or recorded demos/ screencast to be integrated in the videos. Incorporate
one round of internal and client feedback.
- In case of AI/software/tool-based courses, suggest relevant freeware. Write/review and test
the codes to check.
- Review readings (4-6 per course, each up to 1200 words) and confirm technical accuracy of
the content, suggest edits and updates as required. Incorporate one round of internal and
client feedback.
- Create hands-on activities (1-2 lab or any other client preferred format) per course.
Incorporate one round of internal and client feedback.
- Review practice quiz and graded assessments (5 files, each comprising 5-10 questions) and
suggest suitable edits, confirm technical accuracy. Incorporate one round of internal and
client feedback.
- Record talking head videos (onsite/virtually on Zoom) for each course. There will be approx
20-25 minutes of video recording involved per course. Incorporate one round of internal
and client feedback.
Provide digital signatures to be included on the client platform.
For all reviews - validate the content accuracy and provide
recommendations/suggestions, write/re-write to fill content gaps as necessary,
write/test codes and labs, incorporate 1 round of internal feedback and 2 rounds of
client feedback.
- Be available for Client discussions and content discussions as and when required.
Qualifications:
- 5+ years of hands-on experience in MLOps, ML Engineering, or DevOps
roles.
- Deep expertise in Python and test-driven development (TDD) for machine
learning workflows.
- Strong command of containerization and orchestration using Docker and
Kubernetes.
- Experience working with cloud-native ML deployments across AWS, GCP, or
Azure.
- Practical experience designing secure APIs, implementing cloud security
guardrails, and managing scalable inference pipelines.
- Familiarity with GPU-based compute environments, CI/CD pipelines, and
monitoring tools.
Nice-to-Have:
- Prior experience creating or teaching professional-level technical education
content.
- Contributions to open-source MLOps frameworks or DevSecOps
methodologies.
- Recognized certifications such as:
- Certified Kubernetes Administrator (CKA)
- AWS Certified Machine Learning Specialty
- Cloud Security Certifications (e.g., CCSK, CISSP)
Key Competencies:
- Pedagogical clarity and content leadership
- High-quality code writing and review skills
- Strong on-camera communication and presentation
- Instructional design sensitivity with technical rigor
- Collaboration across multidisciplinary teams
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