Artificial Intelligence Engineer

Job Title: AI/ML Engineer - RAG, SLM & Distributed Data (Spanner)

Type: W2 Contract (C2C considered)

Duration: 9-10 months, with potential for extension

Compensation: Up to $60/hr (budget fixed)

Reason for Opening: Addressing a skill gap within the current team.

Interview Process: Two rounds expected.

Start Date: Immediate, targeting early April or sooner.

Key Requirements:

• Experience in the Healthcare industry.
• 3-5 years of overall software engineering experience, with 1-2 years focused on AI/ML Engineering, specifically LLM- or RAG-based systems.
• Proficiency in Python.
• Experience with embedding models, vector search, frameworks like LangChain or LlamaIndex, and API Development (FastAPI/Flask preferred).
• Familiarity with Google Cloud Spanner.
• Understanding of Distributed System fundamentals.
• Comfort working in cloud environments, with a preference for GCP.

Desirable Skills:

• Experience with fine-tuning or quantizing small language models.
• Familiarity with evaluation metrics for retrieval systems (e.g., Recall@K).
• Knowledge of Google Cloud services such as Vertex AI, Pub/Sub, and Dataflow.
• Experience optimizing AI inference for cost and latency.
• Exposure to CI/CD pipelines.

Job Summary: We are looking for a skilled mid-level AI Engineer to contribute to the design and development of production-ready RAG features. This role involves building retrieval pipelines, integrating language models, and developing backend services. You will collaborate with senior engineers and product teams to embed AI capabilities into APIs and user workflows, supported by distributed data systems like Cloud Spanner. Responsibilities include evaluating and enhancing retrieval quality, optimizing model performance, and managing inference costs and latency. You will also play a role in improving the reliability, scalability, and operational practices for applied Generative AI systems in production environments.