AI Engineer

Job Title: AI Engineer

Location: Bangalore, India

About Us:

Meraki Labs stands at the forefront of India's deep-tech innovation landscape, operating as a dynamic venture studio established by the visionary entrepreneur Mukesh Bansal. Our core mission revolves around the creation and rapid scaling of AI-first and truly "moonshot" startups, nurturing them from their nascent stages into industry leaders. We achieve this through an intensive, hands-on partnership model, working side-by-side with exceptional founders who possess both groundbreaking ideas and the drive to execute them.

Currently, Meraki Labs is channeling its significant expertise and resources into a particularly ambitious endeavor: a groundbreaking EdTech platform. This initiative is poised to revolutionize the field of education by democratizing access to world-class STEM learning for students globally. Our immediate focus is on fundamentally redefining how physics is taught and experienced, moving beyond traditional methodologies to deliver an immersive, intuitive, and highly effective learning journey that transcends geographical and socioeconomic barriers. Through this platform, we aim to inspire a new generation of scientists, engineers, and innovators, ensuring that cutting-edge educational resources are within reach of every aspiring learner, everywhere.

Role Overview:

As an AI Engineer on the Capacity team, you will design, build, and deploy the intelligent systems that power our AI Tutor and Simulation Lab.

You’ll collaborate closely with prompt engineers, product managers, and full-stack developers to build scalable AI features that connect language, reasoning, and real-world learning. This is not a traditional ML ops role,  it’s an opportunity to engineer how intelligence flows across the product: from tutoring interactions to real-time physics reasoning.

Your Core Impact

• Build the AI backbone that drives real-time tutoring, contextual reasoning, and simulation feedback.
• Translate learning logic and educational goals into deployable, scalable AI systems.
• Enable the AI Tutor to think, reason, and respond based on structured academic material and live learner inputs.

Key Responsibilities:

1. AI System Architecture & Development

• Design and develop scalable AI systems that enable chat-based tutoring, concept explainability, and interactive problem solving.
• Implement and maintain model-serving APIs, vector databases, and context pipelines to connect content, learners, and the tutor interface.
• Contribute to the design of the AI reasoning layer that interprets simulation outputs and translates them into learner-friendly explanations.

2. Simulation Lab Intelligence

• Work with the ML team to integrate LLMs with the Simulation Lab; enabling the system to read experiment variables, predict outcomes, and explain results dynamically.
• Create evaluation loops that compare student actions against expected results and generate personalized feedback through the tutor.
• Support the underlying ML logic for physics-based prediction and real-time data flow between lab modules and the tutor layer.

3. Model Integration & Optimization

• Fine-tune, evaluate, and deploy LLMs or smaller domain models that serve specific platform functions.
• Design retrieval and grounding workflows so that all model outputs reference the correct textbook or course material.
• Optimize performance, latency, and scalability for high-traffic, interactive learning environments.

4. Collaboration & Research

• Partner with Prompt Engineers to ensure reasoning consistency across tutoring and simulations.
• Work with Product and Education teams to define use cases that align AI behavior with learning goals.
• Stay updated with new model capabilities and research advancements in RAG, tool use, and multi-modal learning systems.

5. Data & Infrastructure

• Maintain robust data pipelines for model inputs (textbooks, transcripts, lab data) and evaluation sets.
• Ensure privacy-safe data handling and continuous model performance tracking.
• Deploy and monitor AI workloads using cloud platforms (AWS, GCP, or Azure).etc.

Soft Skills:

• Strong problem-solving and analytical abilities.
• Eagerness to learn, innovate and deliver impactful results.
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Required Qualifications & Skills

• 3–4 years of experience in AI engineering, ML integration, or backend systems for AI-driven products.
• Strong proficiency in Python, with experience in frameworks like FastAPI, Flask, or LangChain.
• Familiarity with LLMs, embeddings, RAG systems, and vector databases (Pinecone, FAISS, Chroma, etc.).
• Experience building APIs and integrating with frontend components.
• Working knowledge of cloud platforms (AWS, GCP, Azure) and model deployment environments.
• Understanding of data structures, algorithms, and OOP principles.