Machine Learning Engineer Intern (Unpaid, Remote/Hybrid)
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Ekasys Inc.
has a strong history of delivering IT solutions across defense and commercial sectors. With an expanded focus on Artificial Intelligence, we are building retrieval-augmented and generative AI pipelines that integrate vector search, multimodal processing, and large language models. We are seeking a Machine Learning Engineer Intern to contribute to the design and integration of GenAI systems into real-world applications.
Key job responsibilities
- Develop and refine retrieval-augmented generation (RAG) pipelines for AI assistants
- Work with embeddings, vector databases, and memory systems
- Assist with prompt engineering, reranking, and model evaluation
- Experiment with multimodal pipelines combining text, structured data, and images
- Collaborate with engineers to integrate ML workflows into backend systems and cloud environments
A day in the life
As a Machine Learning Engineer Intern, you will experiment with embeddings, integrate vector search systems, and contribute to context-aware pipelines. You’ll learn how GenAI systems are designed, evaluated, and deployed using orchestration tools and cloud platforms.
About The Team
The Ekasys AI team focuses on applied Generative AI, combining retrieval systems, memory architectures, and large language models to deliver robust, domain-specific assistants and intelligent applications.
Basic Qualifications
- Bachelor’s or Master’s degree in Computer Science, Data Science, AI/ML, or related field (in progress or completed)
- Strong programming skills in Python
- Foundational understanding of core ML and NLP concepts
Preferred Qualifications
- Experience with GenAI frameworks such as LangChain/LangGraph, LlamaIndex, or similar.
- Familiarity with vector databases (FAISS, Pinecone, Milvus, Weaviate, Chroma)
- Exposure to embedding models (Hugging Face, OpenAI, Qwen, etc.)
- Knowledge of
Ollama
for LLM orchestration
- Experience with PyTorch, TensorFlow, or Hugging Face Transformers
- Cloud exposure:
Azure AI services
, AWS, or GCP Vertex AI
- MLOps tools: MLflow, Weights & Biases
Note:
You are not required to have all listed skills. A solid Python foundation and an eagerness to learn GenAI workflows are most important.