À propos
1. Model Integration: Deploy and optimize LLMs, embeddings, and RAG pipelines into production applications.
2. AI Microservices: Build scalable Python-based services for knowledge extraction, question answering, and domain-specific reasoning.
3. Evaluation & Monitoring: Implement frameworks to measure precision, recall, latency, and drift; set up alerts for model degradation.
4. Prompt & Context Engineering: Design context windows, prompt templates, and fine-tuning strategies for insurance-specific tasks.
5. Data Pipelines: Collaborate with data scientists to create deterministic, auditable flows from raw documents to model-ready inputs.
6. Security & Compliance: Ensure models are deployed with guardrails, logging, and data isolation to meet SOC2/HIPAA standards.
7. Cross-Functional Collaboration: Partner with product, compliance, and engineering to ship features that balance innovation with reliability.
What We're Looking For
1. Experience: 4+ years in applied AI/ML engineering, with production experience (not just prototypes).
2. Core Skills: Deep proficiency with Python, FastAPI/Flask, and ML libraries (Transformers, LangChain, PyTorch/TensorFlow).
3. LLM Expertise: Hands-on experience integrating LLM APIs, fine-tuning, embeddings, or retrieval systems.
4. Cloud & Infra: Familiarity with deploying AI workloads on AWS/Azure/GCP; exposure to vector DBs (Pinecone, Weaviate, FAISS).
5. Evaluation Mindset: Experience building structured evaluation and monitoring for AI systems.
6. Startup DNA: Comfortable with ambiguity and rapid iteration; thrives in lean environments.
Nice to Have
1. Background in insurance or financial services data.
2. Experience with orchestration frameworks (LangGraph, Airflow, Prefect).
3. Familiarity with secure deployment patterns (VPC, single-tenant isolation, audit logging).
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Compétences linguistiques
- English
Avis aux utilisateurs
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