AI/ML Software Engineer

About

We are seeking a highly skilled AI/ML Software Engineer with strong programming experience in Python to design, develop, and deploy intelligent software systems that leverage Artificial Intelligence and Machine Learning techniques.

This role involves working on cutting-edge solutions such as LLM agents, RAG systems, chatbots, document intelligence, and AI-powered automation tools.

Responsibilities

AI/ML Development & System Design

• Design and develop AI/ML-powered applications for:
  • Document analysis, redaction, and generation
  • Chatbots and conversational AI
  • Knowledge retrieval using LLM agents
  • Translation, transcription, and data processing
• Build and optimize RAG (Retrieval-Augmented Generation) systems
• Design multi-agent AI systems and task-oriented workflows
• Evaluate when to use LLM vs non-LLM approaches

Engineering & Architecture

• Develop production-grade backend systems using Python
• Build APIs, middleware, and scalable data pipelines
• Work with service-oriented architecture and microservices
• Integrate AI solutions into existing enterprise systems

Deployment & Operations

• Deploy AI/ML solutions in hybrid cloud environments
• Work with containerized applications (Docker)
• Optimize applications for low-resource environments (limited GPU)
• Maintain and monitor production AI systems

Required Qualifications

• Bachelor’s degree in: Computer Science / Engineering / Data Science / Mathematics or related field
• Strong programming experience in Python
• Solid understanding of: Data structures & algorithms
• Clean coding principles

Required Experience

• SQL and relational database systems (e.g., PostgreSQL)
• Fine-tuning small language models or embedding models
• Contributing to or maintaining open-source software projects
• Graph databases or graph extensions (e.g., Neo4j, Apache AGE)
• Designing and implementing multi-agent or task-oriented AI systems
• Embedding models, vector similarity, re-ranking, and graph retrieval techniques in RAG systems
• Version control systems (e.g., Git), containerization technologies (e.g., Docker), and service-oriented architectures
• Collaborating with large language models (LLMs), including both API-based integration and local deployment
• Validating AI-generated outputs, mitigating hallucinations, and integrating AI tools into production service pipelines