Note: The job is a remote job and is open to candidates in USA. Precision Technologies is seeking a Gen AI / ML Engineer to design and develop advanced machine learning and generative AI applications for enterprise use. The role involves collaborating with various stakeholders to deliver AI-driven solutions while implementing best practices in model development and deployment.
Responsibilities
- Design, develop, and deploy Machine Learning and Generative AI applications for enterprise use cases
- Build AI-powered applications using Large Language Models (LLMs) such as GPT, Claude, Gemini, Llama, and Mistral
- Develop Retrieval-Augmented Generation (RAG) pipelines using vector databases, embeddings, and semantic search techniques
- Fine-tune, evaluate, and optimize foundation models and machine learning models for production environments
- Develop AI/ML APIs and microservices using Python, FastAPI, Flask, or Django
- Build data pipelines for model training, inference, monitoring, and continuous model improvement
- Implement prompt engineering, AI agents, autonomous workflows, and model evaluation strategies
- Integrate AI applications with enterprise systems, cloud services, and third-party APIs
- Collaborate with Data Scientists, Data Engineers, Software Developers, Product Managers, and business stakeholders to deliver AI-driven solutions
- Implement MLOps best practices including model versioning, CI/CD, monitoring, deployment automation, and performance optimization
- Document AI architectures, model performance, technical solutions, and production deployment processes
Skills
- Design, develop, and deploy Machine Learning and Generative AI applications for enterprise use cases
- Build AI-powered applications using Large Language Models (LLMs) such as GPT, Claude, Gemini, Llama, and Mistral
- Develop Retrieval-Augmented Generation (RAG) pipelines using vector databases, embeddings, and semantic search techniques
- Fine-tune, evaluate, and optimize foundation models and machine learning models for production environments
- Develop AI/ML APIs and microservices using Python, FastAPI, Flask, or Django
- Build data pipelines for model training, inference, monitoring, and continuous model improvement
- Implement prompt engineering, AI agents, autonomous workflows, and model evaluation strategies
- Integrate AI applications with enterprise systems, cloud services, and third-party APIs
- Collaborate with Data Scientists, Data Engineers, Software Developers, Product Managers, and business stakeholders to deliver AI-driven solutions
- Implement MLOps best practices including model versioning, CI/CD, monitoring, deployment automation, and performance optimization
- Document AI architectures, model performance, technical solutions, and production deployment processes
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