Machine Learning Operations (MLOps) Engineer
for sections like About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. Only include sections that have real specific content. We must not repeat job title. Must not include Job ID etc. Must preserve all substantive info. Remove boilerplate: cookie/navigation/legal/EEO/equal-opportunity/scam-warning boilerplate, duplicated lines, application instructions. Also remove "Why Gallatin?" maybe? It's not a required heading but could be part of About the role? The posting includes sections: About Gallatin (intro), What You'll Do (responsibilities), What We're Looking For (requirements/qualifications/skills), Mission and Identity (maybe about company), Why Gallatin? (benefits? Actually it's more about mission), Compensation (pay and benefits), Equal opportunity etc (boilerplate to remove). Also note about citizenship requirement etc maybe keep as part of requirements? It's a requirement: U.S. citizenship required. Should keep that.
We need to decide sections: About the role (maybe combine About Gallatin and What You'll Do? Actually About the role could be summary of role. But we have "What You'll Do" which is responsibilities. Could make sections: Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. Also maybe "About the company" not required but could be included as About the role? The instruction: use for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule). So we can include About the role as a heading if we have content. We have About Gallatin intro; could treat as About the role? Might be okay.
We must not create sections with filler like "commensurate with experience" etc. Pay section: we have compensation range $80K - $210K and mention competitive compensation commensurate with experience, equity grant, healthcare, 401k, unlimited PTO. So we can include Pay and Benefits sections. Benefits: healthcare, 401k, unlimited PTO, equity grant, perks. Pay: salary range.
Schedule: not mentioned; we should not create Schedule section unless present. So omit.
We need to preserve all substantive info: responsibilities list, requirements list, qualifications, skills, nice-to-have, mission, identity, etc. Must not invent or summarize away detail. Must keep details.
We need to format lists with - . For prose use
.
We must not include boilerplate: EEO, equal opportunity, scam warning, etc. Also remove "Why Gallatin?" maybe it's not boilerplate but it's mission/motivation; could be part of About the role or separate section? Could include as "About the role" maybe. But we already have About Gallatin intro. Could combine.
We need to avoid duplication: there are repeated lines like "We are building the system..." appears in Mission and Identity and Why Gallatin? Might be duplicate; we should keep only one instance? The instruction: remove duplicated lines. So we need to deduplicate.
Let's extract substantive content:
Intro: About Gallatin: "At Gallatin, we are rebuilding logistics infrastructure for the national security missions of the United States and allied partners. We build AI systems that determine how logistics decisions are made — not just how they're executed. From factory to foxhole, we operate at the layer where data becomes decisions, and decisions make the advantage."
What You'll Do (Responsibilities):
- Build the systems that move a model out of a notebook and into the hands of a planner.
- Sometimes deploying to an air-gapped rack in a tent instead of a VPC.
- Own the path from training run to deployed capability: the infrastructure it runs on, the release process that ships it, the evaluation harness that proves it works, and the telemetry that tells us when it stops working.
- Our AI/ML team works across retrieval-grounded systems for doctrine and logistics data, document and feature extraction, military symbol recognition, optimization and movement models, and an LLM agent platform.
- This role underpins that work: building the infrastructure, release processes, and evaluation systems that make it shippable and keep it honest in production.
- You will have plenty of room to shape how we build it.
- Training & Serving Infrastructure: Own model training, fine-tuning, and batch inference infrastructure across AWS (SageMaker, EKS) and on-premises GPU hardware. Stand up and tune LLM inference serving: vLLM-class stacks, quantization, continuous batching, KV-cache and throughput sizing. Make the hosted-vs-local call with numbers behind it. Build for DDIL: local inference with configurable fallback, degraded-mode behavior, and sane resource envelopes on hardware we do not get to choose.
- Release & Reproducibility: Build CI/CD for models and pipelines: versioned datasets, a model registry, promotion gates, and rollback that actually works under pressure. Own infrastructure as code, containerization, and GitOps deployment across environments ranging from a dev cluster to a disconnected enclave. Make reproducibility a hard requirement. Any result we put in front of a customer or evaluator must be reproducible from a commit and dataset version.
- Evaluation & Observability: Build and own the evaluation harness: regression suites for retrieval and extraction pipelines, LLM-as-judge pipelines with measured judge-human agreement, and adversarial and held-out sets. Instrument production for drift, latency, cost, retrieval quality, and failure modes, including quiet ones such as a retrieval miss that produces a fluent but wrong answer. Make our metrics defensible to external test and evaluation reviewers. “We think it’s good” is not a deliverable.
- Data & Pipeline Ownership: Own ingestion, versioning, and lineage for logistics and doctrinal data drawn from a heterogeneous set of authoritative sources. Build and operate embedding and feature pipelines, incremental indexing, and the unglamorous systems that keep a retrieval index fresh. Build the human-in-the-loop infrastructure: confidence-scored routing, review queues, and feedback capture that improves the next model.
- Secure & Accredited Deployment: Deploy and operate ML systems in IL5 and IL6 environments, including air-gapped or restricted-network enclaves. Build the release, observability, artifact-management, and incident-response workflows those environments require. Support ATO and continuous-authorization work with implementation evidence tied to applicable security controls (NIST SP 800-171, NIST SP 800-53 Rev. 5, CMMC Level 2, FIPS 140-3, and RMF/eMASS). Handle CUI and classified data correctly without being asked twice.
What We're Looking For (Requirements/Qualifications/Skills):
- Strong Platform & Infrastructure Skills:
- 5+ years in MLOps, ML platform, or infrastructure engineering, including meaningful time working on systems with real users.
- Strong Python skills and comfort in a production codebase, not just notebooks.
- Deep Kubernetes and containerization experience, plus infrastructure as code.
- Production experience with AWS ML/Azure infrastructure (SageMaker, EKS, or equivalent).
- Hands-on GPU infrastructure experience: scheduling, utilization, memory sizing, and cost.
- Hands-on experience deploying and operating production software in IL5 or IL6 environments, including disconnected or restricted-network deployments.
- Production ML Judgment:
- You have shipped an LLM or ML system to production and then had to keep it working.
- You know what breaks.
- You have built evaluation and monitoring for ML systems rather than adopting a vendor dashboard and hoping.
- You can reason about where a pipeline’s quality actually comes from and say so when a metric is measuring the wrong thing.
- Ownership:
- You are comfortable with ambiguity and owning a domain end to end.
- This is a small team; there is no one to hand the pager to.
- You are willing to learn the mission domain.
- The engineers who do best here become genuinely interested in the logistics problem itself.
- Nice to Have:
- Clearance: Preferred
- LLM serving and inference optimization (vLLM, TensorRT-LLM, quantization, prefix caching).
- Retrieval-grounded systems in production: hybrid retrieval, re-ranking, index freshness, and citation quality.
- Edge, on-premises, or disconnected deployment.
- Experience supporting ATO, continuous authorization, or production operations in classified environments.
- Defense, intelligence, or another accredited or regulated environment.
- Palantir Foundry, PostgreSQL/pgvector, NATS/JetStream, or ArgoCD.
- A degree in CS, engineering, or a related technical field, or the equivalent built the hard way.
Mission and Identity (maybe about company):
- We are building the system that enables faster, smarter logistics decisions in contested environments, and we're doing it with a team of seasoned entrepreneurs, operators, and technologists who have built and scaled solutions in this space before.
- We hold ourselves to an extremely high standard.
- We value clear thinking, direct communication, and the kind of ownership that doesn't stop until something actually works.
- Our mission is to create decision advantage when the stakes are the highest.
- If we succeed, the system doesn't just run; it gets smarter.
- We're not building AI for its own sake. We're building it because faster, smarter decisions in the most demanding environments on earth can't wait.
- If you want to work somewhere the stakes are real and the mission is urgent — you'll fit in here.
Why Gallatin? (maybe benefits/mission):
- The logistics infrastructure that supports America's warfighters and humanitarian disaster responders is overdue for transformation, and we are building it.
- From defense operations to disaster response, we're solving the hardest problems that keep missions moving when it matters most.
- Join a team where the mission is the point.
Compensation and Benefits:
- Compensation: Gallatin offers competitive compensation commensurate with experience. Actual compensation may vary based on experience, skills, and location.
- In addition to base salary, we offer a generous equity grant, full healthcare coverage, 401k, unlimited PTO, and the perks of working in a high-caliber, mission-driven environment.
- Compensation Range: $80K - $210K
Boilerplate to remove:
- Equal opportunity employer statement.
- Citizenship requirement? Actually that is a requirement: "Note: Due to the nature of certain government contracts held by this organization, U.S. citizenship is a requirement for all positions at Gallatin. Proof of citizenship will be required prior to employment if selected." This is substantive; we should keep it as a requirement.
- Also "We comply with the United States Department of Labor's Pay Transparency provision." Might be boilerplate but could be kept? It's legal; maybe remove as boilerplate. The instruction: remove cookie/navigation/legal/EEO/equal-opportunity/scam-warning boilerplate. So legal statements like Pay Transparency provision could be considered legal boilerplate; better to remove. Also "Gallatin is an equal opportunity employer..." remove.
Also "This position may require the ability to obtain and maintain a U.S. government security clearance. The successful candidate must be able to work in a classified environment when necessary." This is relevant to requirements; keep.
Now structure sections:
We could have:
About the role
At Gallatin, we are rebuilding logistics infrastructure for the national security missions of the United States and allied partners. We build AI systems that determine how logistics decisions are made — not just how they're executed. From factory to foxhole, we operate at the layer where data becomes decisions, and decisions make the advantage.
Responsibilities
- Build the systems that move a model out of a notebook and into the hands of a planner; sometimes deploying to an air-gapped rack in a tent instead of a VPC.
- Own the path from training run to deployed capability: the infrastructure it runs on, the release process that ships it, the evaluation harness that proves it works, and the telemetry that tells us when it stops working.
- . For prose use
. We must not include boilerplate: EEO, equal opportunity, scam warning, etc. Also remove "Why Gallatin?" maybe it's not boilerplate but it's mission/motivation; could be part of About the role or separate section? Could include as "About the role" maybe. But we already have About Gallatin intro. Could combine. We need to avoid duplication: there are repeated lines like "We are building the system..." appears in Mission and Identity and Why Gallatin? Might be duplicate; we should keep only one instance? The instruction: remove duplicated lines. So we need to deduplicate. Let's extract substantive content: Intro: About Gallatin: "At Gallatin, we are rebuilding logistics infrastructure for the national security missions of the United States and allied partners. We build AI systems that determine how logistics decisions are made — not just how they're executed. From factory to foxhole, we operate at the layer where data becomes decisions, and decisions make the advantage." What You'll Do (Responsibilities): - Build the systems that move a model out of a notebook and into the hands of a planner. - Sometimes deploying to an air-gapped rack in a tent instead of a VPC. - Own the path from training run to deployed capability: the infrastructure it runs on, the release process that ships it, the evaluation harness that proves it works, and the telemetry that tells us when it stops working. - Our AI/ML team works across retrieval-grounded systems for doctrine and logistics data, document and feature extraction, military symbol recognition, optimization and movement models, and an LLM agent platform. - This role underpins that work: building the infrastructure, release processes, and evaluation systems that make it shippable and keep it honest in production. - You will have plenty of room to shape how we build it. - Training & Serving Infrastructure: Own model training, fine-tuning, and batch inference infrastructure across AWS (SageMaker, EKS) and on-premises GPU hardware. Stand up and tune LLM inference serving: vLLM-class stacks, quantization, continuous batching, KV-cache and throughput sizing. Make the hosted-vs-local call with numbers behind it. Build for DDIL: local inference with configurable fallback, degraded-mode behavior, and sane resource envelopes on hardware we do not get to choose. - Release & Reproducibility: Build CI/CD for models and pipelines: versioned datasets, a model registry, promotion gates, and rollback that actually works under pressure. Own infrastructure as code, containerization, and GitOps deployment across environments ranging from a dev cluster to a disconnected enclave. Make reproducibility a hard requirement. Any result we put in front of a customer or evaluator must be reproducible from a commit and dataset version. - Evaluation & Observability: Build and own the evaluation harness: regression suites for retrieval and extraction pipelines, LLM-as-judge pipelines with measured judge-human agreement, and adversarial and held-out sets. Instrument production for drift, latency, cost, retrieval quality, and failure modes, including quiet ones such as a retrieval miss that produces a fluent but wrong answer. Make our metrics defensible to external test and evaluation reviewers. “We think it’s good” is not a deliverable. - Data & Pipeline Ownership: Own ingestion, versioning, and lineage for logistics and doctrinal data drawn from a heterogeneous set of authoritative sources. Build and operate embedding and feature pipelines, incremental indexing, and the unglamorous systems that keep a retrieval index fresh. Build the human-in-the-loop infrastructure: confidence-scored routing, review queues, and feedback capture that improves the next model. - Secure & Accredited Deployment: Deploy and operate ML systems in IL5 and IL6 environments, including air-gapped or restricted-network enclaves. Build the release, observability, artifact-management, and incident-response workflows those environments require. Support ATO and continuous-authorization work with implementation evidence tied to applicable security controls (NIST SP 800-171, NIST SP 800-53 Rev. 5, CMMC Level 2, FIPS 140-3, and RMF/eMASS). Handle CUI and classified data correctly without being asked twice. What We're Looking For (Requirements/Qualifications/Skills): - Strong Platform & Infrastructure Skills: - 5+ years in MLOps, ML platform, or infrastructure engineering, including meaningful time working on systems with real users. - Strong Python skills and comfort in a production codebase, not just notebooks. - Deep Kubernetes and containerization experience, plus infrastructure as code. - Production experience with AWS ML/Azure infrastructure (SageMaker, EKS, or equivalent). - Hands-on GPU infrastructure experience: scheduling, utilization, memory sizing, and cost. - Hands-on experience deploying and operating production software in IL5 or IL6 environments, including disconnected or restricted-network deployments. - Production ML Judgment: - You have shipped an LLM or ML system to production and then had to keep it working. - You know what breaks. - You have built evaluation and monitoring for ML systems rather than adopting a vendor dashboard and hoping. - You can reason about where a pipeline’s quality actually comes from and say so when a metric is measuring the wrong thing. - Ownership: - You are comfortable with ambiguity and owning a domain end to end. - This is a small team; there is no one to hand the pager to. - You are willing to learn the mission domain. - The engineers who do best here become genuinely interested in the logistics problem itself. - Nice to Have: - Clearance: Preferred - LLM serving and inference optimization (vLLM, TensorRT-LLM, quantization, prefix caching). - Retrieval-grounded systems in production: hybrid retrieval, re-ranking, index freshness, and citation quality. - Edge, on-premises, or disconnected deployment. - Experience supporting ATO, continuous authorization, or production operations in classified environments. - Defense, intelligence, or another accredited or regulated environment. - Palantir Foundry, PostgreSQL/pgvector, NATS/JetStream, or ArgoCD. - A degree in CS, engineering, or a related technical field, or the equivalent built the hard way. Mission and Identity (maybe about company): - We are building the system that enables faster, smarter logistics decisions in contested environments, and we're doing it with a team of seasoned entrepreneurs, operators, and technologists who have built and scaled solutions in this space before. - We hold ourselves to an extremely high standard. - We value clear thinking, direct communication, and the kind of ownership that doesn't stop until something actually works. - Our mission is to create decision advantage when the stakes are the highest. - If we succeed, the system doesn't just run; it gets smarter. - We're not building AI for its own sake. We're building it because faster, smarter decisions in the most demanding environments on earth can't wait. - If you want to work somewhere the stakes are real and the mission is urgent — you'll fit in here. Why Gallatin? (maybe benefits/mission): - The logistics infrastructure that supports America's warfighters and humanitarian disaster responders is overdue for transformation, and we are building it. - From defense operations to disaster response, we're solving the hardest problems that keep missions moving when it matters most. - Join a team where the mission is the point. Compensation and Benefits: - Compensation: Gallatin offers competitive compensation commensurate with experience. Actual compensation may vary based on experience, skills, and location. - In addition to base salary, we offer a generous equity grant, full healthcare coverage, 401k, unlimited PTO, and the perks of working in a high-caliber, mission-driven environment. - Compensation Range: $80K - $210K Boilerplate to remove: - Equal opportunity employer statement. - Citizenship requirement? Actually that is a requirement: "Note: Due to the nature of certain government contracts held by this organization, U.S. citizenship is a requirement for all positions at Gallatin. Proof of citizenship will be required prior to employment if selected." This is substantive; we should keep it as a requirement. - Also "We comply with the United States Department of Labor's Pay Transparency provision." Might be boilerplate but could be kept? It's legal; maybe remove as boilerplate. The instruction: remove cookie/navigation/legal/EEO/equal-opportunity/scam-warning boilerplate. So legal statements like Pay Transparency provision could be considered legal boilerplate; better to remove. Also "Gallatin is an equal opportunity employer..." remove. Also "This position may require the ability to obtain and maintain a U.S. government security clearance. The successful candidate must be able to work in a classified environment when necessary." This is relevant to requirements; keep. Now structure sections: We could have:
About the role
At Gallatin, we are rebuilding logistics infrastructure for the national security missions of the United States and allied partners. We build AI systems that determine how logistics decisions are made — not just how they're executed. From factory to foxhole, we operate at the layer where data becomes decisions, and decisions make the advantage.
Responsibilities
- Build the systems that move a model out of a notebook and into the hands of a planner; sometimes deploying to an air-gapped rack in a tent instead of a VPC.
- Own the path from training run to deployed capability: the infrastructure it runs on, the release process that ships it, the evaluation harness that proves it works, and the telemetry that tells us when it stops working.