Senior Backend & AI/ML Engineer (Python / FastAPI / EdTech)

Role Overview

We are seeking a versatile Backend & AI/ML Engineer to architect, build, and scale a next-generation examination and adaptive learning platform. In this role, you will primarily own production backend services, high-integrity workflows, database architecture, and platform APIs using Python and FastAPI, while integrating intelligent AI/ML capabilities, behavioral analytics, and generative AI features into the core application.

The ideal candidate brings strong backend engineering rigormanaging complex data models, secure RBAC, and zero-downtime database migrationsalongside the ability to deploy predictive models, recommendation engines, and LLM-powered features for personalized education.

Key Responsibilities

Core Backend Engineering (Primary Focus)

• API & Workflow Development: Build and maintain scalable, asynchronous FastAPI services for administrative and candidate-facing applications across examination management, question paper generation, paper moderation, translation, center management, attendance, and result processing.
• Database Architecture & Migrations: Design and optimize PostgreSQL data models, complex queries, indexing strategies, and transactional integrity. Author and manage safe Alembic database migrations.
• Integrations & Security: Implement secure authentication/authorization (JWT, RBAC), input validation, secure file handling, and integrations with Redis, S3-compatible object storage, biometric systems, email services, and offline center-local systems.
• Reliability & Testing: Apply consistent response handling, custom exception structures, and validation schemas. Write comprehensive unit, integration, and API tests using Pytest.
• Production Operations: Diagnose API performance bottlenecks, concurrency constraints, and data consistency issues. Collaborate with the Technical Lead and DevOps team on safe deployments, rollback strategies, and Kubernetes-based environments.

AI/ML & Adaptive Learning Capabilities

• Behavioral & Predictive Intelligence: Develop and integrate algorithms that assess learner/candidate behavior, engagement, and performance trends (e.g., academic progress tracking, risk/dropout prediction, and feedback loops).
• Recommendation Engines: Build algorithms to suggest adaptive learning paths, subject-specific focus areas, and mentor/study-partner matching based on performance and user context.
• Generative AI & LLM Integration: Leverage LLMs, LangChain, or LlamaIndex to implement retrieval-augmented generation (RAG) for automated explanations, adaptive content generation, and intelligent administrative assistance.
• Model Deployment & Monitoring: Package and serve machine learning and GenAI pipelines as low-latency microservices/RESTful APIs integrated directly into the primary FastAPI backend.

Required Skills & Qualifications

• Backend & Python Mastery: Strong core Python programming skills with hands-on production experience using FastAPI, Pydantic, SQLAlchemy, and Alembic.
• Database Management: Deep knowledge of PostgreSQL, including complex schema design, ACID transactions, query optimization, indexing, and constraint enforcement.
• API Design & Security: Proven track record of designing asynchronous RESTful APIs with strong security standards (JWT, RBAC, payload sanitization, and audit logging).
• AI/ML & GenAI Fundamentals: Practical experience integrating ML models or Generative AI frameworks (e.g., LangChain, LlamaIndex, OpenAI/HuggingFace APIs) into backend architectures.
• Testing & Tooling: Proficient with Pytest, Docker, Git, and automated testing strategies for multi-tier applications.
• Workflow Ownership: Strong ability to handle state-heavy, multi-role business workflows while maintaining data consistency.

Preferred Skills

• Experience with Redis, Celery/task queues, background workers, and event-driven architectures.
• Hands-on experience developing recommendation engines, predictive scoring models, or behavioral analytics in the education, examination, or EdTech domains.
• Familiarity with AWS/Azure/GCP, S3-compatible storage, biometrics, offline sync, or Kubernetes-based deployment environments.
• Exposure to RAG architectures, vector databases (e.g., pgvector, Pinecone), or fine-tuning open-source LLMs.

Success Measures

• Core backend services are robust, secure, tested, and backward-compatible.
• Relational data models and Alembic migrations execute smoothly with zero data loss or workflow interruptions.
• AI/ML services are seamlessly embedded into core platform workflows with reliable API latency.
• High code coverage, clear documentation, and rapid root-cause resolution for production incidents.