Entry-Level AI Engineer

About the Role

This is a full-time, entry-level AI Engineer position within Precision Technologies' Return-to-Work Program. The program is designed for professionals restarting their careers after a break, offering support and opportunities.

Key Responsibilities

• Design, develop, implement, and deploy AI/ML solutions for enterprise applications and real-world business use cases.
• Build AI-powered solutions utilizing Generative AI, Large Language Models (LLMs), Prompt Engineering, and Retrieval-Augmented Generation (RAG).
• Develop AI agents, conversational AI applications, chatbots, and intelligent automation solutions.
• Implement solutions using vector databases and vector search technologies.
• Deploy AI solutions on cloud platforms.
• Implement MLOps practices for model evaluation, monitoring, versioning, optimization, and automated pipelines.

Requirements & Tech Stack

• Python
• Machine learning
• Deep learning
• Data structures
• Algorithms
• Statistical modeling
• Generative AI
• Large Language Models (LLMs)
• Prompt Engineering
• Retrieval-Augmented Generation (RAG)
• AI/ML frameworks and libraries: TensorFlow, PyTorch, Scikit-learn, Keras, NumPy, Pandas
• LLM development and fine-tuning: Hugging Face Transformers, OpenAI APIs, LangChain, LlamaIndex
• Natural Language Processing (NLP)
• Computer vision
• Text classification
• Sentiment analysis
• Embeddings
• Semantic search
• Vector databases: Pinecone, FAISS, Weaviate, Chroma, Milvus
• Cloud platforms: AWS, Microsoft Azure, Google Cloud Platform (GCP)
• REST APIs
• Microservices
• Docker
• Kubernetes
• Model serving
• CI/CD pipelines
• MLOps
• Model evaluation
• Model monitoring
• Model versioning
• Model optimization
• Automated ML pipelines
• MLflow
• Kubeflow
• Cloud-native ML platforms
• Agile/Scrum methodologies

Qualifications

• Experience in designing, developing, implementing, and deploying AI/ML solutions.
• Expertise in Python, machine learning, deep learning, data structures, algorithms, and statistical modeling.
• Experience with Generative AI, Large Language Models (LLMs), Prompt Engineering, and Retrieval-Augmented Generation (RAG).
• Experience with AI/ML frameworks and libraries such as TensorFlow, PyTorch, Scikit-learn, Keras, NumPy, and Pandas.
• Experience developing and fine-tuning LLMs and transformer-based models.
• Understanding of Natural Language Processing (NLP), computer vision, text classification, sentiment analysis, embeddings, and semantic search.
• Experience building AI agents, conversational AI applications, chatbots, and intelligent automation solutions.
• Experience with vector databases and vector search technologies for knowledge retrieval and RAG implementations.
• Experience deploying AI solutions on cloud platforms such as AWS, Microsoft Azure, or Google Cloud Platform (GCP).
• Knowledge of REST APIs, microservices, Docker, Kubernetes, model serving, and CI/CD pipelines.
• Experience with MLOps, model evaluation, monitoring, versioning, model optimization, and automated ML pipelines.
• Understanding of AI ethics, responsible AI, data privacy, model security, bias mitigation, and performance optimization.