Subject Matter Expert

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

1. TDD Python for ML

2. Optimize & Ship ML Code

3. GPU Clusters & Containers

4. Scale with Kubernetes

5. Streamline App Release Workflows

6. Enforce Cloud Security Guardrails

7. Optimize System Maintenance Ops

8. Deploy AI Data Entry

9. 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:

1. Analyze/create learning objectives for each course.

2. Review/create Course Outline for each of the courses.

3. 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.

1. Provide relevant static or recorded demos/ screencast to be integrated in the videos. Incorporate

one round of internal and client feedback.

1. In case of AI/software/tool-based courses, suggest relevant freeware. Write/review and test

the codes to check.

1. 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.

1. Create hands-on activities (1-2 lab or any other client preferred format) per course.

Incorporate one round of internal and client feedback.

1. 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.

1. 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.

1. Provide digital signatures to be included on the client platform.

2. 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.

1. 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