Kces
++AI Research Engineer / Data Scientist (LLM)++ — Mid–Senior
Location:
Morristown, NJ (Tri-State Area Preferred)
Employment Type:
Full-Time | Onsite / Hybrid
Compensation:
$140K — $150K
Are you passionate about building production‑grade
LLM systems, agentic workflows, and retrieval pipelines
that power real‑world applications? We’re looking for a hands‑on
AI Research Engineer / Data Scientist
to lead the design, development, and deployment of cutting‑edge generative AI solutions. In this role, you’ll own the full lifecycle — from proof‑of‑concept to production — while driving architecture, evaluation, scalability, and safety.
What You’ll Do
Lead
POC → pilot → production
lifecycle for LLM and agentic solutions (tool calling, planning, fallbacks).
Architect advanced
retrieval stacks
(chunking, hybrid search, re‑ranking) and hallucination mitigation strategies.
Build
evaluation frameworks
(offline + online, golden sets, CI/CD gates) for correctness, safety, and bias.
Implement
confidence scoring, calibration, abstention policies , and user‑visible citation mechanisms.
Optimize
cost, latency, reliability
(caching, batching, routing, distillation/quantization).
Deploy
secure, observable APIs/services
with RBAC, tracing, and audit logging.
Own
model/vendor selection , prompting vs. fine‑tuning decisions, and roadmap contributions.
Mentor junior team members and align delivery with stakeholder KPIs.
✅ Must‑Have Experience
4–8+ years in
applied ML, data science, or engineering
with shipped LLM products (RAG, tool calling, QA, structured extraction).
Proven track record designing and integrating
evaluation strategies
and CI/CD gates.
Hands‑on experience with
confidence calibration
and abstention/deferral techniques.
Expertise in
retrieval systems
(hybrid search, filters, re‑ranking, chunking) and hallucination mitigation.
Strong
Python engineering (FastAPI) , SQL, containerization, and observability (logs/spans/metrics).
Cloud and vector search experience (Azure/AWS/GCP, Elasticsearch, Pinecone, FAISS).
Nice to Have
Fine‑tuning/LoRA , multi‑agent orchestration, prompt routing.
Advanced uncertainty modeling,
LLM‑as‑a‑judge
techniques.
Model optimization (distillation, quantization) and
GPU/CPU trade‑offs .
Document AI, Databricks, Airflow/Prefect
experience.
Safety and compliance expertise (red‑teaming, model cards, DPIA/PIA).
Domain knowledge in
insurance, finance, or healthcare .
#J-18808-Ljbffr
Location:
Morristown, NJ (Tri-State Area Preferred)
Employment Type:
Full-Time | Onsite / Hybrid
Compensation:
$140K — $150K
Are you passionate about building production‑grade
LLM systems, agentic workflows, and retrieval pipelines
that power real‑world applications? We’re looking for a hands‑on
AI Research Engineer / Data Scientist
to lead the design, development, and deployment of cutting‑edge generative AI solutions. In this role, you’ll own the full lifecycle — from proof‑of‑concept to production — while driving architecture, evaluation, scalability, and safety.
What You’ll Do
Lead
POC → pilot → production
lifecycle for LLM and agentic solutions (tool calling, planning, fallbacks).
Architect advanced
retrieval stacks
(chunking, hybrid search, re‑ranking) and hallucination mitigation strategies.
Build
evaluation frameworks
(offline + online, golden sets, CI/CD gates) for correctness, safety, and bias.
Implement
confidence scoring, calibration, abstention policies , and user‑visible citation mechanisms.
Optimize
cost, latency, reliability
(caching, batching, routing, distillation/quantization).
Deploy
secure, observable APIs/services
with RBAC, tracing, and audit logging.
Own
model/vendor selection , prompting vs. fine‑tuning decisions, and roadmap contributions.
Mentor junior team members and align delivery with stakeholder KPIs.
✅ Must‑Have Experience
4–8+ years in
applied ML, data science, or engineering
with shipped LLM products (RAG, tool calling, QA, structured extraction).
Proven track record designing and integrating
evaluation strategies
and CI/CD gates.
Hands‑on experience with
confidence calibration
and abstention/deferral techniques.
Expertise in
retrieval systems
(hybrid search, filters, re‑ranking, chunking) and hallucination mitigation.
Strong
Python engineering (FastAPI) , SQL, containerization, and observability (logs/spans/metrics).
Cloud and vector search experience (Azure/AWS/GCP, Elasticsearch, Pinecone, FAISS).
Nice to Have
Fine‑tuning/LoRA , multi‑agent orchestration, prompt routing.
Advanced uncertainty modeling,
LLM‑as‑a‑judge
techniques.
Model optimization (distillation, quantization) and
GPU/CPU trade‑offs .
Document AI, Databricks, Airflow/Prefect
experience.
Safety and compliance expertise (red‑teaming, model cards, DPIA/PIA).
Domain knowledge in
insurance, finance, or healthcare .
#J-18808-Ljbffr