Applied AI Engineer, Core Platform
Lumbra, Arlington, TX, United States
About the Role
Lumbra is building Nebula, an agentic harness that makes AI agents reliable, evaluable, and useful for real analytical work in high-consequence environments. We're looking for an Applied AI Engineer to own the intelligence layer of the harness: how agents reason, call tools, retrieve knowledge, and produce trustworthy outputs. You'll build production systems and research prototypes in equal measure, working at the boundary of what's possible with artificial intelligence today.
What You'll Own
Design and evolve our
provider-agnostic LLM integration layer , maintaining a unified abstraction over streaming, tool calling, structured output, and context management that lets the platform run multi-turn, multi-agent conversations across models with no configuration changes.
Develop
advanced agent orchestration techniques
that combine deterministic and non-deterministic tool use to produce complex, multi-step action chains. Outputs must be traceable, interpretable, and grounded in robust, verifiable citations.
Build
MCP servers
over internal data sources and systems, designing agent-native interfaces that go beyond API wrappers to reduce ambiguity, improve determinism, and give agents first-class access to organizational knowledge.
Extend our
MCP‑based tool interaction framework
with capabilities like structured citations, multimodal tool output, and rich provenance metadata that let agents reason transparently about the results they consume.
Architect the
agentic memory layer
for short-, medium-, and long‑term recall across tasks and projects, spanning vector, text, graph, and filesystem stores. Design retrieval strategies that efficiently surface the right context to agents at scale.
Preferred Qualifications
Experience building
production agentic systems , including the infrastructure for reliability, observability, and evaluation, not just prompt engineering
Background in
evaluation methodology
for AI systems: offline benchmarks, human‑in‑the‑loop review, inter‑annotator agreement, or similar quality measurement frameworks
Familiarity with
prompt engineering at scale : template management, few‑shot optimization, chain‑of‑thought patterns, and systematic prompt testing
Prior work with the
Model Context Protocol (MCP)
or similar tool‑use standards for agent‑to‑system interaction
Experience with
information retrieval systems : search ranking, relevance scoring, or hybrid keyword and semantic search
Understanding of
AI safety practices : output filtering, hallucination detection, constrained generation, or grounding techniques
Prior work in
analytical domains
(intelligence, investigations, due diligence, research, competitive intelligence, or similar) that informs what "useful AI" means for practitioners making high‑stakes decisions
Benefits
Comprehensive medical, dental, and vision plans
Premiums 100% covered by Lumbra
for all employees
Exceptionally low premiums for spouses and dependents
Basic life insurance and disability 100% covered for all employees by Lumbra
Option to purchase additional life insurance available
Take the time off that you need, when you need it' paid time off, not accrual based
Generous company holiday calendar including a holiday shutdown in December
Supportive leave of absence program including time off for military service, medical events, and parental leave
Full 401(k) retirement plan
for all full‑time eligible employees
Company‑funded commuter benefits
Free access to on‑site gym at office
#J-18808-Ljbffr
Lumbra is building Nebula, an agentic harness that makes AI agents reliable, evaluable, and useful for real analytical work in high-consequence environments. We're looking for an Applied AI Engineer to own the intelligence layer of the harness: how agents reason, call tools, retrieve knowledge, and produce trustworthy outputs. You'll build production systems and research prototypes in equal measure, working at the boundary of what's possible with artificial intelligence today.
What You'll Own
Design and evolve our
provider-agnostic LLM integration layer , maintaining a unified abstraction over streaming, tool calling, structured output, and context management that lets the platform run multi-turn, multi-agent conversations across models with no configuration changes.
Develop
advanced agent orchestration techniques
that combine deterministic and non-deterministic tool use to produce complex, multi-step action chains. Outputs must be traceable, interpretable, and grounded in robust, verifiable citations.
Build
MCP servers
over internal data sources and systems, designing agent-native interfaces that go beyond API wrappers to reduce ambiguity, improve determinism, and give agents first-class access to organizational knowledge.
Extend our
MCP‑based tool interaction framework
with capabilities like structured citations, multimodal tool output, and rich provenance metadata that let agents reason transparently about the results they consume.
Architect the
agentic memory layer
for short-, medium-, and long‑term recall across tasks and projects, spanning vector, text, graph, and filesystem stores. Design retrieval strategies that efficiently surface the right context to agents at scale.
Preferred Qualifications
Experience building
production agentic systems , including the infrastructure for reliability, observability, and evaluation, not just prompt engineering
Background in
evaluation methodology
for AI systems: offline benchmarks, human‑in‑the‑loop review, inter‑annotator agreement, or similar quality measurement frameworks
Familiarity with
prompt engineering at scale : template management, few‑shot optimization, chain‑of‑thought patterns, and systematic prompt testing
Prior work with the
Model Context Protocol (MCP)
or similar tool‑use standards for agent‑to‑system interaction
Experience with
information retrieval systems : search ranking, relevance scoring, or hybrid keyword and semantic search
Understanding of
AI safety practices : output filtering, hallucination detection, constrained generation, or grounding techniques
Prior work in
analytical domains
(intelligence, investigations, due diligence, research, competitive intelligence, or similar) that informs what "useful AI" means for practitioners making high‑stakes decisions
Benefits
Comprehensive medical, dental, and vision plans
Premiums 100% covered by Lumbra
for all employees
Exceptionally low premiums for spouses and dependents
Basic life insurance and disability 100% covered for all employees by Lumbra
Option to purchase additional life insurance available
Take the time off that you need, when you need it' paid time off, not accrual based
Generous company holiday calendar including a holiday shutdown in December
Supportive leave of absence program including time off for military service, medical events, and parental leave
Full 401(k) retirement plan
for all full‑time eligible employees
Company‑funded commuter benefits
Free access to on‑site gym at office
#J-18808-Ljbffr