AI Engineer

• Design and build agentic AI workflows that can plan and execute complex, multi-step tasks
• Build and maintain RAG solutions end-to-end
• Implement retrieval evaluation and continuous improvement loops
• Integrate tools and data sources using MCP or equivalent standard interfaces
• Deploy and operate AI services on AWS with strong operational practices
• Engineer robust runtime behavior including error handling, retries, fallbacks
• Enable workflow automation and integration by identifying high-value repetitive processes
• Create and maintain technical documentation for architectures, interfaces, evaluation approaches
• Collaborate cross-functionally with product/business stakeholders to translate workflows into well-scoped deliverables

• Manage mission-critical collaboration and content management systems
• Integrate AI capabilities such as intelligent search, workflow automation, and context-aware assistance
• Collaborate closely with stakeholders, system owners, security teams, and end users
• Stay current with Department AI initiatives and systems
• Design, develop, test, implement, and deploy SharePoint infrastructure and solution components
• Drive innovation within the M365 and SharePoint environments
• Conduct stakeholder consultations and workshops

M365 Automation and AI Developer

• Design and build agentic AI workflows that can plan and execute complex, multi-step tasks
• Build and maintain RAG solutions end-to-end
• Implement retrieval evaluation and continuous improvement loops
• Integrate tools and data sources using MCP or equivalent standard interfaces
• Deploy and operate AI services on AWS
• Engineer robust runtime behavior
• Enable workflow automation and integration
• Create and maintain technical documentation
• Collaborate cross-functionally with product/business stakeholders
• Other duties as assigned

• Design and build agentic AI workflows that can plan and execute complex, multi-step tasks
• Build and maintain RAG solutions end-to-end
• Implement retrieval evaluation and continuous improvement loops
• Integrate tools and data sources using MCP or equivalent standard interfaces
• Deploy and operate AI services on AWS with strong operational practices
• Engineer robust runtime behavior including error handling, retries, fallbacks, circuit breakers, and guardrails
• Enable workflow automation and integration by identifying high-value repetitive processes
• Create and maintain technical documentation for architectures, interfaces, evaluation approaches, and operational runbooks
• Collaborate cross-functionally with product/business stakeholders to validate outcomes against measurable success criteria
• Other duties as assigned.

• Turn quantitative prototypes into reusable tools, services, packaged desktop applications, interfaces, and workflow products that can move from internal demo to client pilot to scaled offer.
• Ship across both cloud-hosted services and locally distributed desktop applications, including Electron-based apps when the workflow or client environment calls for it.
• Build enterprise hardening into the productization layer, including authentication, role-based access control (RBAC), observability, security, release quality, cost controls, and deployment discipline.
• Build evaluation, regression, and release discipline into the productization layer so model logic and agent behavior remain measurable as systems change.
• Work closely with the quant lead so model logic, evaluation intent, and governance requirements survive the move into production.
• Make pragmatic architecture choices across large language models (LLMs), deterministic rules, and hybrid systems based on value, latency, cost, and reliability.
• Help shape repeatable build patterns so strong prototypes become faster, more reliable, and more reusable over time.

QuantAI Engineer – Trading, Investment & Optimization

• Define and drive the vision, strategy, and roadmap for enterprise semantic data and data connectivity across business domains
• Partner with business and IT leaders to identify high-value opportunities to connect and standardize data across systems
• Position the semantic data product as a product, not just a technical asset, with clear customers, outcomes, adoption goals, and success metrics
• Define and evolve the product’s value proposition, ensuring alignment to business priorities and measurable outcomes
• Act as the general manager of the product, balancing user needs, technical feasibility, strategic priorities, operational constraints, and business impact
• Define and manage data products aligned to key business domains, including clear ownership, users, and success metrics
• Define target users and use cases for data products, ensuring alignment to specific decisions, workflows, and business outcomes
• Partner closely with business customers to ensure data products support critical decisions, workflows, and operational outcomes
• Drive adoption and usage of shared data assets across business units
• Identify and prioritize use cases where connected data unlocks measurable business value (e.g., improved decisioning, automation, personalization)
• Ensure solutions are aligned to real-world workflows and deliver tangible outcomes
• Lead the development and evolution of enterprise ontologies, taxonomies, and data models that represent key business concepts and relationships
• Establish a scalable semantic layer that enables reuse across analytics, AI, and operational use cases
• Ensure alignment of definitions across domains to reduce fragmentation and duplication
• Support both structured and unstructured data integration, enabling downstream AI and analytics applications
• Ensure data is discoverable, understandable, and usable across teams through consistent definitions and relationships
• Partner deeply with business domains to understand workflows, decision points, and data dependencies, acting as a product owner for how data supports those functions
• Own the end-to-end product lifecycle from discovery and definition to delivery and iteration
• Define product requirements, success metrics, and release plans
• Continuously refine the semantic data product based on user feedback and evolving business needs
• Define critical metrics to measure adoption, usability, and impact of the semantic data product
• Supervise usage and continuously improve accessibility and value delivery
• Evangelize the value of connected semantic data across the organization
• Partner with data governance teams to establish standards, definitions, and data quality expectations
• Promote consistency, reusability, and scalability of data assets across the enterprise

Senior Manager, AI Lead – Semantic Layer

• Advise and make recommendations to senior IT and business leaders on areas for service simplification.
• Define and maintain system architecture and technology stack.
• Collaborate with product managers, designers, business intelligence engineers, and other stakeholders to make technical decisions, define technical requirements, and create development roadmaps in service of business goals.
• Conduct code reviews, provide constructive feedback, and promote continuous improvement in code quality and development processes.
• Mentor engineers and enforce best practices.
• Contribute hands-on across the stack.
• Collaborate with DevOps for CI/CD and cloud deployments.
• Participate in hiring and onboarding new team members.

Principal AI Engineer

• Design and implement AI solutions that operate across multi-cloud and hybrid environments, including Azure, Google Cloud Platform (GCP), and AWS.
• Evaluate and recommend hosting, infrastructure, and deployment architectures based on mission requirements, security posture, scalability, portability, and long-term maintainability — not tied to a single cloud provider.
• Architect AI/ML platforms and applications using cloud-native and containerized technologies that minimize vendor lock-in and maximize interoperability.
• Build and modernize web applications using modern full-stack frameworks and AI assisted development tooling to accelerate delivery and improve developer productivity.
• Develop sophisticated conversational AI and knowledge management solutions that enable users to securely interact with enterprise data using natural language.
• Design and implement Retrieval-Augmented Generation (RAG) architectures using vector databases, semantic search, knowledge graphs, and document processing pipelines.
• Lead the design of secure cloud and on-premises AI infrastructure solutions that meet Federal Government and internal compliance requirements.
• Define architectural design decisions affecting infrastructure selection, orchestration, model hosting, observability, scalability, and system behavior for AI enabled solutions.
• Analyze client requirements and emerging AI technology trends to recommend scalable, compliant, and future-proof architectures.
• Participate in design thinking and solution engineering activities to solve complex mission and operational challenges.
• Lead whiteboarding and technical strategy sessions with clients, partners, and internal teams.
• Communicate findings and recommendations to both technical and non-technical stakeholders, translating complex AI and infrastructure concepts into actionable guidance.
• Ensure infrastructure and application architectures support secure software delivery practices including CI/CD, Infrastructure as Code (IaC), automated testing, and model lifecycle management.

• Architect and deliver end-to-end AI solutions — RAG pipelines, agentic workflows, multi-modal applications, that are modular, interoperable, and designed for portability across cloud providers and on-premises environments.
• Lead LLM-agnostic design decisions: evaluate and integrate models from OpenAI, Anthropic, Meta (Llama), Mistral, Cohere, and open-source alternatives based on mission fit, not vendor preference.
• Design and implement AI agents and multi-agent orchestration using frameworks such as LangChain, LlamaIndex, AutoGen, CrewAI, or custom implementations, with clear separation of concerns to enable future platform migration.
• Build and optimize retrieval systems using vector databases (Pinecone, Weaviate, pgvector, OpenSearch) and hybrid search strategies not tied to a single managed service.
• Own the full AI lifecycle: data ingestion, embedding strategy, chunking, prompt engineering, evaluation, fine-tuning (LoRA/PEFT), and continuous monitoring.
• Design infrastructure using IaC (Terraform, Pulumi) with multi-cloud support across Azure (OpenAI, AI Search, AKS), AWS (Bedrock, SageMaker), and GCP (Vertex AI) — enabling lift-and-shift without architectural rework.
• Apply semantic chunking, knowledge graph integration, and structured data fusion to improve retrieval fidelity in complex government data environments.
• Implement robust evaluation frameworks (RAGAS, LLM-as-judge, human-in-the-loop review) to measure and improve AI system quality continuously.
• Engage clients directly whiteboard sessions, solution design reviews, and stakeholder presentations translating complex AI architecture into clear mission outcomes.
• Ensure all AI solutions comply with federal standards (FedRAMP, FISMA, NIST AI RMF, NIST 800-53) and apply responsible AI practices including bias detection and model explainability.

• Design and implement intelligent agent architectures that can reason, plan, and take actions using LangChain, LangGraph, and AutoGen
• Develop and deploy multi-agent systems using Model Context Protocol (MCP) and Agent-to-Agent (A2A) protocols to facilitate communication, tool usage, and collaborative task solving
• Build advanced RAG pipelines integrating unstructured data with Knowledge Graphs (KG) to enhance reasoning accuracy and context retention
• Fine-tune SLMs for specific domains and optimize them for edge device performance, including ONNX, GGML, or Ollama
• Develop evaluation frameworks to test agent reliability, safety, and performance, moving from prototype to production, including ReAct loops and human-in-the-loop

• As a Senior AI Engineer focused on Agentic Evaluation and Verification and Validation (V&V), you will join the AI and Data Science team within Slingshot’s Research and Development organization.
• You will contribute to advancing how intelligent systems are evaluated and validated for mission-critical space operations.
• This role focuses on building and scaling evaluation frameworks, benchmarks, and simulation-backed validation systems for agentic AI systems, including multi-step, tool-using, and autonomous decision-making workflows powered by LLMs and reinforcement learning.
• Your work will directly support the development of reliable and trustworthy autonomous mission planning systems.
• You will partner closely with AI researchers and domain experts to translate real-world mission concepts into structured, testable evaluation systems.

Senior AI Engineer, Agentic Evaluation – V&V

• Own strategy and evolution of platform capabilities that enable AI assistants, workflows, and integrations.
• Define how internal AI systems and external partners connect to FUB data through well-designed tools and integration patterns.
• Shape intelligence capabilities that help AI better understand agent and lead activity.
• Lead product direction for memory capabilities that make AI experiences more personalized over time.
• Partner closely with engineering, AI/ML, design, and product peers to assess tradeoffs and define solutions.
• Help identify and prioritize highest-value platform opportunities based on product needs and long-term strategy.
• Drive alignment across teams by communicating roadmap priorities and progress clearly to stakeholders and leadership.
• Contribute to long-term vision for how AI platform capabilities show up in product experiences across web, mobile, automation, and assistant surfaces.

Senior Product Manager, AI Platform – Ecosystem

• Play a key role in building the internal platform which supports training and deploying AI models across the entire organization.
• Take ownership of defining the platform to enable AI model fine-tuning and batch inference by building the SDKs and supporting the infra.
• Manage cross-cutting stakeholder relationships, prioritizing customer needs first.
• Navigate ambiguous, hairy, and technical problem spaces.
• Jump into domains, languages, and problem areas that might be new and unfamiliar.

Senior Software Engineer II, ML/AI Platform

• Build AI systems that power a scientific discovery platform for our federal client.
• Develop conversational interfaces that allow users to access complex, multi-domain scientific datasets using natural language.
• Design AI agents that automate experimental data processing at scale.
• Implement retrieval‑augmented generation (RAG) architectures to transform decades of scientific research into actionable intelligence.
• Collaborate with stakeholders to solve one of the client’s 26 “lighthouse challenges” requiring faster, smarter decision-making.
• Work across technologies such as AWS Bedrock, Databricks, vector databases, and advanced prompt‑engineering techniques.
• Build agent frameworks supporting scientific discovery in areas like subsurface geology, supply chain intelligence, and materials science.

• Architect, develop, and deploy advanced autonomous AI agents tailored for cybersecurity applications.
• Define and implement intelligent agent systems capable of real-time decision-making and adaptive threat responses.
• Collaborate cross-functionally with product managers, engineers, and analysts to translate innovative concepts into robust solutions.
• Mentor and guide engineering teams on best practices in developing agent-based AI solutions.
• Continuously evaluate, test, and enhance the capabilities and performance of agentic systems within Whitespace’s ecosystem.
• Drive technical excellence and promote engineering best practices within the AI team.

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