S
Lead AI Engineer & Technical Architect
Scalis LLC
2 hours ago
Full-time
On-site
New York City, New York, United States
Bold Business is an AI-first U.S. based global workforce solutions company rebuilding how businesses operate in the AI era. We build AI-Amplified Talent™, custom AI agents, and automation systems for clients like AT&T, JP Morgan, and Verizon — and we back every engagement. If you want to build things that ship and matter, you'll fit right in.
The Role You are the
technical standard-setter and primary builder
for how AI systems are designed, built, and shipped at Bold.
You will:
Architect systems
Build them yourself
Set the bar for everyone else
If you haven’t
personally shipped production AI systems end-to-end
, this role will not be a fit.
What You’ll Do Build Real Systems
Design and ship
production RAG pipelines
(end-to-end)
Build
multi-agent systems
used by real clients
Integrate
LLMs into production workflows
(not prototypes)
Own
backend systems (FastAPI, multi-tenant, secure)
Deploy with
Docker + CI/CD + cloud
Measure and improve
output quality before anything ships
Set the Standard
Define how AI systems are built at Bold
Establish
architecture patterns, prompting standards, and data flows
Own
code quality and reviews
— nothing ships without your approval
Choose
tools, frameworks, and infrastructure
Mentor engineers and
raise the performance bar across the team
Non-Negotiable Requirements
Advanced to fluent English — written and spoken
Ability to explain technical concepts clearly to non-technical stakeholders: clients, founders, business owners
Professional written communication across emails, Slack, documentation, and status updates
Comfortable on video calls and async communication with a U.S.-based team
RAG (This Defines the Role)
You have shipped
2+ production RAG systems
(not demos)
You can explain
every decision
(chunking, embeddings, retrieval, re-ranking)
You’ve worked with
vector databases in production
You’ve debugged real issues (hallucination, low recall, bad retrieval)
You’ve built
multi-tenant systems with clean data separation
You know how to
measure and improve output quality
Core Engineering
5+ years
Python in production
3+ years building
AI/LLM systems used by real users
LangChain / LangGraph
in production (not tutorials)
Strong backend experience (FastAPI or equivalent)
Security fundamentals (auth, secrets, isolation)
Proven ability to
set and enforce engineering standards
Strong Signals (Highly Valued)
LlamaIndex, hybrid search, or re-ranking models
RAG over complex data (PDFs, tables, images)
MLOps, evaluation, monitoring
Experience leading technical direction for a team
Who You Are
You
build and think in systems
, not just features
You don’t wait to be unblocked — you solve it
You care about
clean, scalable systems in production
You can explain complex systems simply
You push standards higher, even when it’s uncomfortable
Experience:
5–8 years Proof:
Live systems with real users (required)
How We Evaluate You We will ask you to:
Walk through a system you built in detail
Build a
working RAG pipeline
Show how you think, not just what you know
Bold Business AI-First. Standards-First. Delivery-First. We build systems that
ship, scale, and earn trust
.
Preferred Skills Experience with Machine Learning Frameworks
#J-18808-Ljbffr
The Role You are the
technical standard-setter and primary builder
for how AI systems are designed, built, and shipped at Bold.
You will:
Architect systems
Build them yourself
Set the bar for everyone else
If you haven’t
personally shipped production AI systems end-to-end
, this role will not be a fit.
What You’ll Do Build Real Systems
Design and ship
production RAG pipelines
(end-to-end)
Build
multi-agent systems
used by real clients
Integrate
LLMs into production workflows
(not prototypes)
Own
backend systems (FastAPI, multi-tenant, secure)
Deploy with
Docker + CI/CD + cloud
Measure and improve
output quality before anything ships
Set the Standard
Define how AI systems are built at Bold
Establish
architecture patterns, prompting standards, and data flows
Own
code quality and reviews
— nothing ships without your approval
Choose
tools, frameworks, and infrastructure
Mentor engineers and
raise the performance bar across the team
Non-Negotiable Requirements
Advanced to fluent English — written and spoken
Ability to explain technical concepts clearly to non-technical stakeholders: clients, founders, business owners
Professional written communication across emails, Slack, documentation, and status updates
Comfortable on video calls and async communication with a U.S.-based team
RAG (This Defines the Role)
You have shipped
2+ production RAG systems
(not demos)
You can explain
every decision
(chunking, embeddings, retrieval, re-ranking)
You’ve worked with
vector databases in production
You’ve debugged real issues (hallucination, low recall, bad retrieval)
You’ve built
multi-tenant systems with clean data separation
You know how to
measure and improve output quality
Core Engineering
5+ years
Python in production
3+ years building
AI/LLM systems used by real users
LangChain / LangGraph
in production (not tutorials)
Strong backend experience (FastAPI or equivalent)
Security fundamentals (auth, secrets, isolation)
Proven ability to
set and enforce engineering standards
Strong Signals (Highly Valued)
LlamaIndex, hybrid search, or re-ranking models
RAG over complex data (PDFs, tables, images)
MLOps, evaluation, monitoring
Experience leading technical direction for a team
Who You Are
You
build and think in systems
, not just features
You don’t wait to be unblocked — you solve it
You care about
clean, scalable systems in production
You can explain complex systems simply
You push standards higher, even when it’s uncomfortable
Experience:
5–8 years Proof:
Live systems with real users (required)
How We Evaluate You We will ask you to:
Walk through a system you built in detail
Build a
working RAG pipeline
Show how you think, not just what you know
Bold Business AI-First. Standards-First. Delivery-First. We build systems that
ship, scale, and earn trust
.
Preferred Skills Experience with Machine Learning Frameworks
#J-18808-Ljbffr