Backend and AI/ML Engineer

Backend and AI/ML Engineer

Responsibilities

• Design, develop, and maintain backend systems, services, and APIs to support platform functionality.
• Build and integrate scalable file processing pipelines for document ingestion, chunking, and metadata extraction.
• Develop and deploy machine learning models for semantic search, document embeddings, and summarization.
• Implement vector similarity search using tools like Milvus, FAISS, or Pinecone.
• Collaborate with cross-functional teams, including frontend developers and product managers, to deliver end-to-end solutions.
• Optimize the performance and scalability of backend and AI/ML systems.
• Ensure system security and implement authentication mechanisms such as OAuth and JWT.
• Research and experiment with the latest AI/ML approaches to improve platform intelligence and user experience.
• Monitor and improve the performance of deployed models, retraining as necessary.
• Document code, processes, and technical decisions to ensure maintainability.

Requirements

• Programming Skills

  : Proficiency in

  Python

  and

  Node.js

  . Familiarity with frameworks such as

  Flask and Nodejs

• Backend Knowledge

  : Strong experience with RESTful APIs, microservices architecture, and database systems (e.g.,

  MongoDB

  ).

• AI/ML Expertise

  : Hands-on experience with machine learning frameworks such as

  TensorFlow

  ,

  PyTorch

  , or

  Scikit-learn

  . Proficiency in NLP tools like

  Hugging Face

  ,

  SpaCy

  , or

  NLTK

  .

• Vector Search

  : Familiarity with vector databases like

  Milvus

  ,

  FAISS

  for implementing semantic search.

• Cloud and Deployment

  : Experience with deploying backend and AI/ML solutions using cloud platforms (AWS, GCP, Azure) and containerization tools like

  Docker

  and

  Kubernetes

  .

• Version Control and CI/CD

  : Knowledge of tools like

  Git

  ,

  DVC

  ,

  MLflow

  , or

  Kubeflow

  for managing pipelines and deployment workflows.

• Performance Optimization

  : Skills in optimizing both backend services and AI/ML models for performance and scalability.

Preferred Skills

• Prior experience building AI-powered applications.
• Understanding of large language models (e.g., GPT, BERT) and their fine-tuning.
• Familiarity with secure coding practices and data privacy regulations.
• Knowledge of tools for managing machine learning workflows in production (

  Apache Nifi,

  Airflow

  ).