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Job Description
Job Location : United States
We are seeking an enthusiastic and motivated AI Junior Engineer to develop GRC (Governance, Risk, and Compliance) use cases at the intersection of design, AI, and enterprise challenges. This role offers an excellent opportunity to gain hands-on experience in building and deploying AI-driven solutions and agents, working with cutting-edge frameworks, and contributing to impactful real-world AI use cases in the GRC domain.
As an AI Junior Engineer, you will work closely with senior engineers to explore, prototype, and validate AI solutions using modern LLM tools, RAG patterns, and agent simulation frameworks. If you have been experimenting with AI on the side, this is your chance to go mainstream and apply those skills in a fast-paced, client-facing environment.
Key Responsibilities
Solutioning problems using user-centric design techniques, leveraging modern architectures and AI.
Build production-ready AI use cases leveraging LLMs, agents, and context engineering solutions.
Design and implement memory and knowledge solutions, including RAG validation with custom scripts and embedding-based similarity checks.
Develop AI-driven information discovery solutions like semantic search using vector databases (e.g., Pinecone, FAISS, Weaviate).
Explore foundation models and fine-tuning techniques (LoRA, DSPy, adapter frameworks) while optimizing performance, cost, and security.
Work with ML libraries (PyTorch, TensorFlow) and experiment with RNN/CNN architectures.
Collaborate with mentors to solution AI/ML use cases in the GRC domain, working directly with stakeholders in client-facing engagements.
Document findings, prototype results, and contribute to knowledge-sharing initiatives in a fast-paced delivery environment.
Skills and Experience
Strong programming skills in Python, Typescript
Familiarity with LLMs, prompt engineering, or agentic workflows
Understanding of LangChain, LangGraph, or similar frameworks (academic, project, or professional exposure).
Knowledge of RAG patterns, embeddings, and similarity checks.
Exposure to vector databases and AI/ML libraries (PyTorch, TensorFlow, Hugging Face).
Curiosity and eagerness to learn about foundation model fine-tuning (LoRA, DSPy) and multi-agent systems.
Strong problem-solving, analytical, and research abilities.
Ability to thrive in a fast-paced, client-facing environment, working with business teams to align technical solutions with real-world needs.
Education
Bachelor’s or Master's degree in Computer Science, Engineering, Data Science, or a related field. Alternatively, if you’ve been self-learning and experimenting independently and are confident in your skills, we’d love to hear from you.
Compensation Data
Compensation will be paid on an hourly basis, determined by knowledge, experience, and location.