AI Engineer

This role is for one of Weekday’s clients
Min Experience: 2 years
Location: Remote (India)
JobType: full-time

Requirements

About the role

We are seeking a talented Gen AI Developer with at least 2 years of experience in NLP and Generative AI development. You'll work on building and enhancing AI-driven features - chatbots, QA agents, and smarter reporting assistants - using tools like LangGraph, LangChain, OpenAI, and Hugging Face. Experience in Django-based chatbot deployment and programmatic marketing is a strong plus.

Responsibilities:

• Design, develop, and deploy NLP and generative AI models using LangGraph, LangChain, OpenAI GPT series, and Hugging Face Transformers.
• Integrate AI workflows to power conversational agents for campaign QA, reporting prompts, and performance recommendations.
• Collaborate with product and data teams to embed GenAI features into AtomicAds' core platform.
• Build and maintain chatbot solutions using Python and Django for user interactions and dashboard integrations.
• Optimize AI pipelines for scalability, latency, reliability, and cost-efficiency.
• Implement model monitoring and evaluation tools to ensure performance and fairness.
• Ensure secure, compliant deployment of models and APIs.
• Maintain clear documentation, testing strategies, and version control.

Requirements:

• Experience: Minimum 2 years in NLP and GenAI development.
• Technical Skills: Proficient in Python, with deep experience in AI frameworks: LangGraph, LangChain, OpenAI API, Hugging Face. Hands-on experience building chatbot/service layers using Django or similar Python web frameworks.
• AI Competency: Strong grasp of LLM architectures, prompt engineering, model fine-tuning, and evaluation.
• Software Engineering: Demonstrated ability to write clean, modular, and testable code.
• Collaboration and Communication: Ability to work in small teams, with clear written and verbal communication.

Bonus:

• Experience with programmatic advertising, DSPs, or marketing automation.
• Familiarity with cloud platforms (AWS, GCP, Azure) for AI workloads
• Knowledge of MLOps, Docker, Kubernetes, or CI/CD pipelines