Job description

Standard Job Description Focuses on research & development of technologies that enable and advance semi and fully autonomous systems for both defense and commercial customers. Serves as the algorithm expert with up-to-date knowledge on modern AI research and may be involved in the inception of ideas and drive the development cycles from research to test of prototypes for a major project or component of a major project.Researches and discovers improvements to machine learning and robotic algorithms; Drives advancements in techniques used in signal processing, computer vision (CV), and control systems; Develops AI algorithms for mission systems; Applies latest research on AI algorithms and trains machine learning / deep learning models to solve a variety of problems; Investigates and applies the latest machine learning and deep learning techniques; Optimizes the performance of AI algorithms, applications, and platforms; Develops and documents algorithm and implementation requirements; Develops prototypes that will enable autonomous functionality in LM products and platforms; Interfaces with other teams involved the development lifecycle for perception, mission and motion planning, simulation and modeling, testing, etc. The Astris AI Sales Engineer serves as the technical backbone of our AI Factory go-to-market motion. This role owns the demo environments, proof-of-concept architectures, and technical narrative that show prospective enterprise customers what AI Factory platform — can do. It combines hands-on AI/ML engineering skill with strong presentation ability to support Account Executives across the full commercial sales cycle, from first technical discovery call through proof-of-concept and close. Demo & Technical Asset Development • Build and maintain reusable MLOps demo environments on Panel covering the full model lifecycle: experiment tracking, versioning, CI/CD for ML, deployment, and production monitoring • Develop industry-specific demo narratives and datasets (predictive maintenance, fraud detection, supply chain forecasting, and similar) that map Panel's capabilities to a prospect's actual workflows • Maintain demo infrastructure, including containerized and Kubernetes-based environments, so demos run reliably across customer meetings, trade shows, and remote sessions • Build reusable technical assets: reference architectures, ROI calculators, solution briefs, and competitive comparison sheets Customer Engagement Support • Partner with Account Executives throughout the commercial sales cycle — qualification through close • Lead technical discovery sessions to understand a prospect's existing infrastructure, data environment, team structure, and integration constraints • Deliver customized demonstrations and technical presentations to audiences ranging from data scientists and ML engineers to CTO/CIO-level executives • Respond to RFIs/RFPs with accurate technical content and MLOps-specific competitive positioning • Build trusted-advisor relationships with customer technical stakeholders and support technical handoffs to Customer Success Engineers and Solution Architects once a deal closes Technical Enablement & Collaboration • Maintain deep expertise in Panel and general MLOps best practices (model versioning, experiment tracking, CI/CD for ML, monitoring, governance) • Stay current on the broader MLOps and Kubernetes ecosystem (Kubeflow, MLflow, KServe, Ray, Argo, and similar) to keep demos and competitive positioning sharp • Collaborate with Product and Engineering to feed customer feedback and market signal into the Panel roadmap • Support partner-channel enablement (ISVs, SIs, cloud partners) with MLOps-focused technical training and co-selling assets Basic Qualifications • 5–9 years in Sales Engineering, Solutions Engineering, Pre-Sales Technical Consulting, or a hands-on ML engineering / MLOps role • Working proficiency in Python and at least one ML framework (PyTorch, TensorFlow, scikit-learn, or similar) • Demonstrated ability to build and maintain demo or POC environments end-to-end Desired Skills • Hands-on experience with MLOps platforms such as Astris AI Factory, MLflow, Kubeflow, Weights & Biases, or similar tools • Working knowledge of generative AI / LLM concepts (RAG, agent frameworks) • Experience selling into manufacturing, supply chain, financial services, or technology verticals • Practical experience with Kubernetes (deploying, debugging, or operating workloads) and containerization Pay Information GeoZone Definition: GeoZones are geographic groupings created by Lockheed Martin to align compensation ranges with regional labor markets and cost-of-labor differences across the United States. Locations are assigned a Geo Zone based on the primary work location of the role. • Full-time salary range (GEOZONE 1): $150800.00 - $280000.00 • Includes metropolitan areas such as Sunnyvale CA; Pal Alto, CA; New York City metropolitan area; Newark, New Jersey; etc. • Full-time salary range (GEOZONE 2): $135700.00 - $251900.00 • Includes metropolitan areas such as Denver, CO; King of Prussia, PA; Stratford, CT; Moorestown, NJ; etc. • Full-time salary range (GEOZONE 3): $120600.00 - $224000.00 • Includes metropolitan areas such as Dallas–Fort Worth, TX; Orlando, FL; Grand Prairie, TX; Marietta, GA; etc. • Full-time salary range (GEOZONE 4): $108600.00 - $201600.00 • Includes metropolitan areas such as Camden, AR; Lexington, KY; Ocala, FL; Lufkin, TX; etc. At Lockheed Martin, we know mission success starts with taking care of our people. Our Total Rewards program is designed to attract top talent, support your well-being, and help you grow—both professionally and personally. The salary range for this position is as listed on the requisition. Please note that the salary information listed is a general guideline only. Lockheed Martin considers factors such as (but not limited to) scope and responsibilities of the position, candidate's work experience, education/ training, key skills as well as market(work location) and business considerations when extending an offer. Benefits offered: Medical, Dental, Vision, Flexible work arrangements and schedules (e.g., 4x10), 401(k) match, Paid time off, Holidays, Parental Leave, EAP, Flexible Spending Accounts, Education Assistance, Life Insurance, Short-Term Disability, and Long-Term Disability. • Annual short-term and/or long-term incentive compensation programs may be offered depending on the position. Payments under these annual programs are not guaranteed and can vary from year to year and are tied to a range of performance metrics. • For (Washington state applicants only) Non-represented full-time employees: accrue at least 10 hours per month of Paid Time Off (PTO) to be used for incidental absences and other reasons; receive at least 90 hours for holidays. Represented full time employees accrue 6.67 hours of Vacation per month; accrue up to 52 hours of sick leave annually; receive at least 96 hours for holidays. PTO, Vacation, sick leave, and holiday hours are prorated based on start date during the calendar year.

Requirements

Must-haves

Standard Job Description
Demo & Technical Asset Development
Build and maintain reusable MLOps demo environments on Panel covering the full model lifecycle: experiment tracking, versioning, CI/CD for ML, deployment, and production monitoring
Develop industry-specific demo narratives and datasets (predictive maintenance, fraud detection, supply chain forecasting, and similar) that map Panel's capabilities to a prospect's actual workflows
Maintain demo infrastructure, including containerized and Kubernetes-based environments, so demos run reliably across customer meetings, trade shows, and remote sessions
Build reusable technical assets: reference architectures, ROI calculators, solution briefs, and competitive comparison sheets
Customer Engagement Support
Partner with Account Executives throughout the commercial sales cycle — qualification through close
Lead technical discovery sessions to understand a prospect's existing infrastructure, data environment, team structure, and integration constraints
Deliver customized demonstrations and technical presentations to audiences ranging from data scientists and ML engineers to CTO/CIO-level executives
Respond to RFIs/RFPs with accurate technical content and MLOps-specific competitive positioning
Build trusted-advisor relationships with customer technical stakeholders and support technical handoffs to Customer Success Engineers and Solution Architects once a deal closes
Technical Enablement & Collaboration
Maintain deep expertise in Panel and general MLOps best practices (model versioning, experiment tracking, CI/CD for ML, monitoring, governance)
Stay current on the broader MLOps and Kubernetes ecosystem (Kubeflow, MLflow, KServe, Ray, Argo, and similar) to keep demos and competitive positioning sharp
Collaborate with Product and Engineering to feed customer feedback and market signal into the Panel roadmap
Support partner-channel enablement (ISVs, SIs, cloud partners) with MLOps-focused technical training and co-selling assets
5–9 years in Sales Engineering, Solutions Engineering, Pre-Sales Technical Consulting, or a hands-on ML engineering / MLOps role
Working proficiency in Python and at least one ML framework (PyTorch, TensorFlow, scikit-learn, or similar)

Nice-to-haves

Hands-on experience with MLOps platforms such as Astris AI Factory, MLflow, Kubeflow, Weights & Biases, or similar tools
Working knowledge of generative AI / LLM concepts (RAG, agent frameworks)
Experience selling into manufacturing, supply chain, financial services, or technology verticals
Practical experience with Kubernetes (deploying, debugging, or operating workloads) and containerization
Pay Information

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aimachine learninggomlci/cdkubernetespythonllm

Platform: eightfold
Effort: medium

Benefits

Benefits detected in the listing.

Dental

Vision

401(K)

Pto

Paid Time Off

Parental Leave