Content Specialist & Teaching Assistant

About Futurense Technologies

Futurense is India's fastest-growing AI skilling company, dedicated to empowering professionals for the next generation of AI-driven careers.

The company operates across four strategic verticals:

• Futurense Uni
• Futurense USP
• Futurense EUP
• Futurense Campus

This role will work closely with Futurense Uni, which partners with prestigious IITs to equip students and working professionals across India with practical, industry-focused skills.

The mission is to ensure that graduates and professionals are job-ready in AI from day one, bridging the gap between academic knowledge and real-world application.

The Role

This role sits at the centre of how learners understand complex technical concepts.

The position combines two key areas:

Research & Content: Researching GenAI, LLMs, and agentic AI concepts and turning them into clear explanations, visuals, diagrams, and mental models that learners can easily understand.

Teaching Support: Working directly with learners during live sessions, clearing doubts, reviewing their work, and supporting them through projects until they understand and can apply the concepts themselves.

Because the AI field evolves rapidly, continuous learning and staying ahead of the latest developments are a core part of the role.

What You Will Do

Research & Stay Current

• Deeply research new models, frameworks, papers, and releases across:
  • Generative AI
  • Large Language Models (LLMs)
  • Agentic AI
  • Forward Deployed Engineering (FDE)
• Track changes and developments across the AI ecosystem.
• Verify technical claims against official documentation before including them in learner-facing content.

Build Technical Explanations

Turn complex technical concepts into intuitive learning experiences using:

• Diagrams
• Analogies
• Visual mental models
• Step-by-step explanations

Key topics may include:

• RAG
• Agent loops
• MCP
• Orchestration frameworks
• Fine-tuning
• LLM-based systems

The goal is to make complex concepts simple and intuitive without sacrificing technical accuracy.

Create Learning Material

Develop learning resources that can be directly used in live cohort sessions, including:

• Pre-reads
• Exercises
• Quizzes
• Solution walkthroughs
• Technical explanations
• Visual learning material

Market & Competitor Research

• Research what other AI education programs are teaching.
• Track where AI hiring demand is moving.
• Identify gaps in the existing curriculum.
• Recommend areas where the curriculum can be strengthened.

Innovate on Pedagogy

• Experiment with better ways to teach, practise, and assess technical concepts.
• Develop learning formats beyond traditional slides and lectures.
• Test new approaches and evaluate their effectiveness.

Support Learners as a Teaching Assistant

• Assist during live sessions.
• Answer learner doubts within committed response times.
• Review assignments and submissions.
• Guide learners through projects when they get stuck.
• Identify areas where learners may need additional support.

Keep Content Current

AI APIs, models, and frameworks change rapidly. You'll be responsible for:

• Auditing existing learning material.
• Identifying outdated information.
• Updating content as frameworks and APIs evolve.
• Ensuring learners receive technically accurate and current information.

Upskill Continuously

Continuous learning is considered a core responsibility.

You will be expected to continuously build your own knowledge of:

• GenAI
• LLMs
• Agentic AI
• Agentic systems
• FDE practices
• New AI frameworks and tools

What We Look For

The ideal candidate should have a strong technical foundation in AI combined with excellent research and communication abilities.

GenAI & LLM Knowledge

• Solid working understanding of GenAI and LLM fundamentals.
• Hands-on experience with at least some parts of the modern AI stack, such as:
  • LangChain
  • LangGraph
  • RAG pipelines
  • Vector databases
  • Agent frameworks

Research Skills

Strong research instincts are important. You should:

• Prefer official documentation over secondary sources.
• Check framework and API version numbers.
• Verify technical information before publishing it.
• Be comfortable researching rapidly changing technologies.

Explanation & Communication

• Ability to take complex technical concepts and explain them simply.
• Strong understanding of how to simplify information without compromising accuracy.
• Ability to create clear explanations, diagrams, analogies, and mental models.

AI-Native Workflow

• Heavy and effective use of AI tools in your own daily workflow.
• Comfortable working with LLMs as part of your research, development, and productivity process.

Evidence We Value

Candidates who can demonstrate what they have built, rather than simply describe their skills, will receive strong preference.

Particularly valuable evidence includes:

• A demonstrable GenAI or agentic AI project.
• A deployed AI product or solution.
• Experience using frameworks such as:
  • LangChain
  • LangGraph
  • CrewAI
• Personal and side projects are fully considered, provided the work is your own and you can clearly explain the technical decisions behind it.

Cloud Deployment Experience

At least one agentic AI or LLM-based solution deployed on a cloud platform such as:

• AWS
• Google Cloud Platform (GCP)
• Microsoft Azure

You should be able to clearly explain how you took the solution from a local prototype to a production system serving real users.

Good to Have

Communication

• Strong written and verbal communication skills.
• Ability to communicate effectively in front of a live learner cohort.

Teaching & Learner Empathy

• Patience and empathy when working with learners.
• Ability to recognize when a learner is struggling, even before they explicitly ask for help.
• Ability to adapt teaching based on the learner's existing knowledge and understanding.

LLM Evaluation

• Hands-on experience with LLM evaluation frameworks such as:
  • RAGAS
• Or experience designing structured LLM evaluation systems independently.

LLM Observability

• Familiarity with platforms such as:
  • LangSmith
  • Langfuse
• Exposure to more than one cloud provider is an advantage.

Teaching & Content Experience

Prior experience in any of the following is beneficial:

• Teaching
• Mentoring
• Technical writing
• Creating technical explanations
• Creating diagrams or educational content

A strong portfolio of work can be more valuable than simply having a certain number of years of experience.

What You Will Gain

Exposure to the AI Frontier

You'll work closely with the latest models, frameworks, and agentic AI patterns alongside:

• Forward Deployed Engineers
• Senior industry practitioners
• Experienced AI professionals

Real Ownership

Your content can reach live learner cohorts within weeks, giving you direct feedback based on actual learner outcomes.

A Valuable Combination of Skills

The role helps develop a combination of:

• Deep AI expertise
• Teaching ability
• Research discipline
• Technical communication

Together, these skills can provide a strong and durable foundation for an AI career.

Career Growth

There is a clear growth path into senior roles in:

• Content
• Curriculum
• Instruction

As the Academy continues to scale, there will be opportunities to take on greater ownership and responsibility.

Why Futurense?

At Futurense Technologies, you'll be part of a fast-paced, hyper-growth startup environment surrounded by people who enjoy moving quickly, thinking big, and building meaningful careers.

The company aims to create an environment where employees can learn continuously, experiment, and grow while working on meaningful challenges.

A Culture That:

• Celebrates learning and encourages employees to take risks and experiment.
• Supports growth while ensuring employees have fun along the way.
• Encourages innovation and freedom, giving people the opportunity to push boundaries and try new ideas.

‍