- Company Name
- Rascal Ventures
- Job Title
- Senior Staff AI Engineer
- Job Description
-
**Job Title:** Senior Staff AI Engineer
**Role Summary:**
Architect and ship a full‑stack AI system combining computer vision, NLP, and semantic search for healthcare, from end‑to‑end design through deployment and scaling on AWS. Own technical strategy, safety frameworks, and mentorship while collaborating closely with the CTO and cross‑functional teams.
**Expectations:**
- Deliver production‑ready ML solutions within tight timelines and ambiguous conditions.
- Champion safety, data quality, and responsible AI without needing approval.
- Mentor junior engineers and shape engineering culture.
- Transition smoothly from fractional engagement to full‑time status.
**Key Responsibilities:**
- Design and implement computer vision pipelines (object detection, image classification).
- Build and maintain retrieval systems using embeddings (Weaviate, Pinecone, Faiss).
- Develop NLP/document understanding workflows (parsing, OCR, transformer inference).
- Engineer batch and real‑time inference architectures, optimize performance, and manage trade‑offs.
- Build and maintain MLOps pipelines (Docker, CI/CD, model versioning, feature stores).
- Configure AWS services (SageMaker, Lambda, ECS) for scalable, fault‑tolerant deployment.
- Define and enforce safety and evaluation metrics; work with CTO on safety frameworks.
- Lead architecture decisions, create patterns for future AI engineers, and onboard new hires.
**Required Skills:**
- 10+ years software engineering; 5+ in deployed ML systems.
- Expert Python programming.
- 5+ years hands‑on AWS (SageMaker, Lambda, ECS).
- Proven experience building CV systems (object detection, classification).
- Experience with embedding‑based retrieval (Weaviate, Pinecone, Faiss).
- NLP and document understanding (parsing, OCR, transformers).
- MLOps fundamentals: Docker, CI/CD, model versioning, feature stores.
- Strong grasp of batch vs real‑time inference trade‑offs.
- Ability to ship under pressure, raise safety concerns, and mentor peers.
**Required Education & Certifications:**
- Bachelor’s or Master’s degree in Computer Science, Electrical Engineering, or related field (or equivalent experience).
- AWS certifications (e.g., Solutions Architect, ML Specialty) preferred but not mandatory.