About Careertakes

👉 Important disclosure: Careertakes is a third-party recruiting platform supporting this hiring process. If selected, you will be employed directly by our client, Information Technology.

Applicants for this role may also receive access to additional matched opportunities through the Careertakes platform.

---

What You’ll Do

This entry-level Generative AI Engineer role puts you on the engineering team building and deploying LLM-powered applications and RAG (Retrieval-Augmented Generation) pipelines for real-world use cases. You will work hands-on with modern LLMs, vector stores, and orchestration frameworks to prototype, evaluate, and productionize solutions.

• Develop and implement Generative AI applications using Large Language Models (LLMs)
• Integrate and call LLM APIs such as OpenAI, Google Gemini, and Hugging Face models
• Design and implement Retrieval-Augmented Generation (RAG) pipelines and semantic search
• Experiment with prompt engineering to improve model responses and accuracy
• Build AI-powered chatbots, virtual assistants, and automation workflows
• Work with frameworks such as LangChain and LlamaIndex to construct pipelines
• Integrate and query vector databases (FAISS, ChromaDB, Pinecone)
• Test, evaluate, and optimize LLM-based applications for latency, cost, and quality
• Collaborate with software engineers and data scientists to build scalable solutions
• Stay current with emerging Generative AI tools, models, and best practices

---

What We’re Looking For

Confidential Client seeks candidates with a solid technical foundation and curiosity about generative models and applied NLP.

• Bachelor’s degree in Computer Science, Information Technology, Artificial Intelligence, or related field (or equivalent practical experience)
• Strong programming skills in Python
• Basic understanding of Machine Learning, Deep Learning, and NLP concepts
• Familiarity with Large Language Models (LLMs) and generative AI fundamentals
• Basic experience or knowledge of prompt engineering
• Understanding of embeddings, vector search, and semantic retrieval
• Strong analytical and problem-solving skills
• Clear communication and ability to collaborate across teams

---

Preferred / Nice-to-have

• Hands-on experience with LangChain, LlamaIndex, or similar orchestration libraries
• Experience integrating vector databases such as FAISS, ChromaDB, or Pinecone
• Prior projects using OpenAI, Hugging Face, or Google Gemini APIs
• Familiarity with evaluation metrics for LLM outputs and automated testing
• Knowledge of deployment tooling (Docker, Kubernetes) and cloud services

---

Compensation & Benefits

• Estimated base compensation: $130,236 per year (estimated figure from source).
• Confidential Client offers competitive compensation and benefits (details provided during the interview process). Benefits may include health insurance, retirement savings, paid time off, and professional development opportunities — specifics vary by hire and location.

---

Location & Work Authorization

• Location: San Diego, CA (on-site or hybrid as defined by Confidential Client).
• Applicants must be authorized to work in the United States. If you require sponsorship, please indicate your status in your application; sponsorship availability is determined by the hiring organization.

---

Equal Opportunity & Hiring Transparency

Careertakes and our client are Equal Opportunity Employers committed to building a diverse and inclusive workforce. We prohibit discrimination or harassment of any kind. To support a fair and efficient hiring process, AI tools may be used to assist with application review or resume screening. These tools do not replace human decision-making. Final hiring decisions are made by people.

If you have questions about how your data is used, please contact us directly.