Senior AI/ML Developer
Moon Technolabs
- Location
- Ahmedabad, Gujarat, India
- Job type
- Full-time
Required skills
- Python
- Artificial Intelligence
- deep learning
- DevOps
- FastAPI
- Flask
- JavaScript
- machine learning
- NLP
- SQL
- TypeScript
About the role
Moon Technolabs
Website:
moontechnolabs.com
Job details:
Sr AI/ML DeveloperExp: 4+ Years Location: Ahmedabad
Programming Languages:- Design, develop, and deploy production-ready AI/ML solutions for enterprise applications.
- Build and optimize Large Language Model (LLM) applications, AI agents, and Retrieval-Augmented Generation (RAG) pipelines.
- Fine-tune, evaluate, and optimize open-source and commercial AI models for specific business use cases.
- Develop scalable APIs and AI services using Python frameworks such as FastAPI or Flask.
- Build intelligent automation workflows using AI, vector databases, and orchestration frameworks.
- Design and implement machine learning pipelines for data preprocessing, feature engineering, model training, validation, and deployment.
- Collaborate with Product Managers, Architects, UI/UX Designers, and DevOps teams to deliver AI-powered products.
- Optimize model inference performance, latency, and infrastructure costs.
- Mentor junior AI engineers and participate in architecture discussions, technical reviews, and code reviews.
- Research and evaluate emerging AI technologies and recommend adoption where appropriate.
- Ensure AI applications follow security, privacy, and responsible AI best practices.
Programming Languages- Strong expertise in Python.
- Good knowledge of SQL.
- Basic understanding of JavaScript or TypeScript for AI integrations.
Artificial Intelligence & Machine Learning- Strong experience with Machine Learning, Deep Learning, NLP, and Generative AI.
- Hands-on experience with LLMs such as GPT, Claude, Llama, Qwen, Mistral, or Gemma.
- Experience developing AI Agents and multi-agent workflows.
- Strong understanding of prompt engineering, structured outputs, function calling, and tool integration.
- Experience implementing Retrieval-Augmented Generation (RAG) architectures.
- Knowledge of embedding models and semantic search.
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