AI Engineer

• Design, develop, and maintain scalable full-stack web applications using modern frameworks and cloud-native architectures
• Build and maintain user interfaces using React and related front-end technologies
• Develop backend services, APIs, serverless functions, and integrations using Python and/or Node.js
• Integrate AI and machine learning services such as Amazon Bedrock and other large language model (LLM) platforms into enterprise applications
• Implement Retrieval-Augmented Generation (RAG), agentic AI workflows, and AI orchestration capabilities
• Optimize AI model prompts, monitor model performance, and support cost-efficient AI solution design
• Support deployment automation, monitoring, troubleshooting, and operational sustainment of AI-enabled systems
• Collaborate with engineers, product owners, and stakeholders to deliver scalable and secure solutions
• Ensure compliance with cybersecurity requirements and cloud governance best practices utilizing AWS IAM, security guardrails, and related controls
• Support DevSecOps activities including CI/CD pipelines, infrastructure automation, and cloud cost management

Full Stack AI Developer

• 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

• Implement proof of concept through production-ready capabilities that directly improve clinical trial processes for a large system supporting cancer clinical trials.
• Design, build, and deploy autonomous AI agents capable of understanding high-level goals, planning actions, and executing tasks with minimal human intervention.
• Utilize frameworks like LangChain, LangGraph or CrewAI to build LLM-powered workflows.
• Build robust backend services (Python/REST API/Node.js) and Spring boot RESTful APIs to support agent orchestration and user interfaces.
• Create intuitive interfaces (React, Next.js, TypeScript) for users to monitor, configure, and intervene in AI agent workflows with human-in-the-loop.
• Develop custom tools, plugins, and Model Context Protocol (MCP) servers to securely expose internal services, APIs, and databases to LLMs.
• Architect and optimize Retrieval-Augmented Generation (RAG) pipelines and vector databases.
• Deploy AI applications on cloud platforms (AWS, Azure, or GCP) using Docker, Kubernetes, and CI/CD pipelines.
• Implement observability, logging, tracing (e.g., LangSmith, Langfuse), and guardrails to monitor AI performance, cost, and safety.

• 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

• Design and develop autonomous AI agents capable of reasoning, planning, and executing tasks independently.
• Build and orchestrate multi-agent workflows using modern AI frameworks and tooling.
• Develop AI-enabled applications using large language models and retrieval-augmented generation (RAG) architectures.
• Integrate AI agents with APIs, databases, enterprise systems, and external services.
• Build conversational and human-in-the-loop interfaces for AI systems.
• Develop observability, tracing, and evaluation pipelines for AI workflows and agent performance.
• Containerize and deploy AI applications using Docker-based infrastructure.
• Collaborate with software engineers, cybersecurity teams, researchers, and stakeholders to deliver scalable AI capabilities.
• Maintain CI/CD pipelines and source control workflows using GitLab.
• Optimize prompts, memory systems, context handling, and agent decision-making.
• Research and prototype emerging agentic AI techniques and frameworks.

AI Developer

• Partner with customer SMEs and operational stakeholders to translate mission workflows and operational requirements into agentic AI solutions, including intelligent agents, workflow specialists, guardrails, tools, and validation frameworks.
• Design, develop, and maintain agent definitions and configurations, including objectives, behavioral instructions, input/output contracts, safety policies, operational constraints, and tool utilization rules.
• Develop workflow-specific AI specialists and reusable agent patterns to support operational and enterprise use cases while maintaining clear functional boundaries and scalable architectures.
• Build and maintain comprehensive AI testing and evaluation suites, including gold-standard input/output validation, adversarial testing, guardrail validation, scope-boundary testing, tool-use validation, regression testing, and multi-turn conversational testing.
• Analyze failed, degraded, or stale agent test results to identify performance gaps, tune agent behavior, improve reliability, and document updates across agent versions and releases.
• Integrate AI agents, orchestration workflows, APIs, and supporting services into existing application and operational environments using modern software engineering and DevSecOps practices.
• Support the deployment, sustainment, troubleshooting, and optimization of AI-enabled applications and agentic workflows across cloud, edge, and hybrid environments.
• Train and mentor customer agent creators on Agent Builder workflows, publishing processes, testing methodologies, maintenance procedures, and operational best practices.
• Develop reusable templates, workflow examples, implementation guides, and best-practice documentation to enable scalable and consistent customer development of agentic AI solutions.
• Collaborate with cross-functional engineering, operations, cybersecurity, and product teams to ensure AI-enabled capabilities align with mission objectives, security requirements, and operational constraints.
• Contribute to continuous improvement efforts for AI-enabled applications, orchestration frameworks, testing methodologies, and software delivery pipelines.

Application/Agentic AI Engineer

• Designing, developing, and deploying generative AI models (e.g., LLMs, diffusion models)
• Architecting and building agentic systems with autonomous decision-making capabilities
• Integrating generative AI and agentic solutions into existing products and services
• Developing and maintaining the software infrastructure required for AI/ML pipeline
• Developing and implementing data pipelines for training and evaluating AI models
• Conducting research and experimentation with new generative AI and agentic techniques
• Other duties as assigned or requested

• Design and implement scalable APIs for agent orchestration and learner interaction
• Build data pipelines that feed agent evaluation and continuous improvement cycles
• Create event streaming infrastructure for real-time learner interaction analysis
• Ensure data quality and accessibility for training and evaluating learning agents
• Improve data infrastructure for cost and performance in close coordination with Data and ML teams
• Design and implement rigorous evaluation frameworks to measure agent performance and improve cycles
• Develop LLM evaluation processes and perform error analysis to identify systemic improvements for agentic learning systems
• Support instrumentation that makes evaluation relevant (metrics, traces/logs, and analysis workflows)
• Participate in on-call rotation and incident response for learning platform reliability
• Contribute to runbooks, postmortems, and reliability improvements as we evolve our operating model

Senior Software AI Engineer

• Design the entire ecosystem for AI solutions
• Define end-to-end architecture for AI systems, ensuring scalability and integration
• Establish best practices for MLOps
• Lead cross-functional collaboration with Data Science, DevOps, and Product teams
• Mentor senior engineers and present architectural decisions to executive leadership

Lead AI Architect

• We build AI agents that actually work in enterprise environments — not prototypes, not demos.
• Engineer's who can own the entire agent stack: a production frontend, a robust backend, a properly secured API and identity layer, a memory architecture that scales, and LLM integrations that are model-agnostic and built to last.
• Deployed on client engagements as the lead technical architect and builder of agentic systems running in AWS, OCI, and Azure.
• Work directly with client stakeholders, translate complex requirements into working systems, and leave behind infrastructure clients can operate and extend.
• Help Trilagen productize our delivery approach as we scale the practice.
• Design and build the entire application — not just the AI layer.
• Own the repo, the CI/CD pipeline, the deployment, and the runbook.
• Understand tradeoffs between platforms and advise clients on where to run what and why.
• Deep, hands-on experience with leading LLM providers and their APIs.
• Architect model-agnostic integration layers.
• Design systems that do real multi-step work: tool use and function calling patterns, agent-to-agent orchestration and handoffs.
• Build the security envelope around every agent system you ship.

• 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

• Design, develop, and deploy scalable AI solutions
• Build LLM-based solutions for enterprise applications
• Integrate LLMs with business systems
• Architect AI agents for intelligent automation
• Implement CI/CD pipelines for machine learning models
• Monitor model performance and governance
• Collaborate with cross-functional teams for solution delivery
• Create technical documentation and mentoring

• 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

• Design, implement, and train reinforcement learning (RL) and multi-agent reinforcement learning (MARL) algorithms for complex decision-making problems
• Develop scalable training pipelines using Python and modern ML frameworks
• Build and evaluate agents in simulated environments using Gym or PettingZoo, high-fidelity simulators, or custom environments
• Apply RL techniques such as policy optimization, value-based learning, model-based RL, and imitation learning
• Collaborate with domain experts to define reward structures, constraints, and evaluation metrics aligned with mission objectives
• Implement distributed training workflows leveraging cloud compute, containerization, and orchestration technologies
• Transition trained models into production systems, following strong software engineering best practices
• Contribute to system architecture and performance optimization in Python with opportunities to extend into C++ or Rust for high-performance components

Reinforcement Learning Engineer

• 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

• Design and implement enterprise-wide solutions, with a focus on HR AI.
• Leverage ServiceNow (including NowAssist) capabilities as well as other AI tools and capabilities (ChatGPTe, Flowise, etc.)
• Lead the design of AI-driven HR chatbot and AI agent use cases
• Collaborate with HR stakeholders to translate requirements into robust architectural designs
• Perform technical analysis and present architecture decisions/recommendations to key stakeholders
• Define integration patterns across systems, ensuring alignment with enterprise standards and protocols
• Monitor and improve AI performance and adoption in HR service delivery
• Contribute to AI strategy and governance within the HR technology ecosystem
• Work directly with HR stakeholders to refine use cases, translating ambiguous needs into clear, scalable designs
• Define how the HR chatbot and AI agents behave—how they answer questions, trigger transactions, and interact with systems like Workday and ChatGPTe
• Design how LLMs connect to APIs, workflows, and data sources—ensuring solutions are practical, secure, and maintainable
• Attend requirements gathering sessions with business stakeholders
• Design complex chatbot flows and agentic AI use cases
• Define integrations and architecture for HR AI solutions (APIs, MCP)
• Make product or technology recommendations for AI solutions, evaluating and selecting appropriate tools and platforms to meet business needs
• Analyze and model technical solutioning options, considering scale, security, UX, business value and cost implications
• Provide technical assessments, highlighting capabilities, limitations, and trade-offs to non-technical stakeholders
• Research industry best practices for AI technologies in HR service delivery
• Work with platform vendor to provide usage feedback and request product changes.
• Knowledge share and collaborate with other technical teams to implement architectural standards across the organization

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