À propos
Raleigh, NC Work Model:
Hybrid –
3 days onsite / 2 days remote About the Role We are looking for a
hands-on Data Scientist
to design, build, and deploy
production-grade Generative AI systems
on AWS. This role goes beyond experimentation—you will architect
secure, scalable, and costefficient GenAI solutions
used by real users in enterprise environments. You will work closely with engineering, data, and product teams to deliver
LLM-powered applications , including
RAG-based document intelligence, chatbots, and AI assistants . Key Responsibilities Architect and implement
Generative AI solutions
using
LLMs (GPT, Claude, Mixtral, etc.) Design and deploy
Retrieval-Augmented Generation (RAG)
pipelines for document Q&A and enterprise search Build
semantic search and embedding pipelines
using vector databases (FAISS, OpenSearch, Pinecone) Select and optimize
LLM models, prompts, and inference strategies
for accuracy, latency, and cost Implement
hallucination mitigation techniques
(grounding, prompt constraints, validation layers) Design
secure, scalable architectures on AWS
(Bedrock, SageMaker, Lambda, API Gateway, S3) Fine-tune models using
PEFT techniques (LoRA, QLoRA)
when required Partner with MLOps teams to productionize models with
CI/CD, monitoring, and rollback Optimize GenAI systems for
cost, latency, and throughput Collaborate onsite with cross-functional teams (3 days/week in Raleigh) Required Skills & Experience Generative AI & LLMs Strong understanding of
LLM architectures and inference Hands-on experience with
RAG systems
in production Prompt engineering, temperature/top-p tuning Knowledge of
LoRA / QLoRA / PEFT
techniques Experience mitigating
hallucinations
and improving factuality Embeddings & Retrieval Chunking strategies and metadata handling Semantic embeddings (Sentence-BERT, OpenAI, etc.) Vector similarity search (cosine, dot-product) Vector databases:
FAISS, OpenSearch, Pinecone AWS & Cloud Architecture AWS AI/ML services:
Bedrock, SageMaker Serverless & APIs:
Lambda, API Gateway Data storage:
S3, DynamoDB Security:
IAM, KMS, VPC, CloudTrail Experience designing
enterprise-grade, compliant systems Programming & Frameworks Python
(strong) Experience with
LangChain, Haystack, FastAPI
(or similar) Familiarity with async processing and caching layers MLOps & Production Model versioning and monitoring CI/CD for ML systems Rollback strategies and drift detection Performance and cost monitoring Qualifications 7+ years
in data science, software/ML engineering, with
2+ years in GenAI/LLMs Proven experience deploying
AI systems to production
Compétences linguistiques
- English
Avis aux utilisateurs
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