AI Engineer

• Report directly to our VP of Agentic AI
• Shape the core intelligence and behavior of the agent — how it interprets intent, plans, assists, and recovers from failure — so that it reliably helps users build working Unqork applications.
• Help scale the backend that powers the agent in production, contributing to a service that stays fast, observable, and resilient as usage grows.
• Drive how we measure and raise agent quality over time, turning real-world usage into a feedback loop that makes the product steadily more capable and trustworthy.

Staff AI Engineer

• Design, build, and operate systems in customer-facing production environments
• Translate ambiguous business and customer problems into prototypes & technical specs
• Track AI capabilities and apply them where they create clear leverage
• Own the full application lifecycle: product discovery, experimentation, evaluation, deployment, and monitoring
• Own system design, tradeoffs, and long-term scaling and maintainability
• Build code bases that are legible to agents and other developers; drive the organization forward on tooling
• Partner with product managers to enhance their product vision, including with AI-native solutions

• Help build next-generation AI-powered product experiences.
• Build AI-driven product features that integrate LLMs (like GPT, Claude, or Gemini) via APIs.
• Prototype quickly and iterate fast, turning rough concepts into working interfaces and tools.
• Develop front-end experiences in React that make complex AI outputs intuitive and delightful.
• Collaborate with backend engineers to design APIs and data pipelines that support AI interactions.
• Work across the stack when needed — from front-end interfaces to backend service integration in Python.
• Experiment with new LLM capabilities, libraries, and prompt engineering approaches.
• Contribute to internal tools for data cleaning, model evaluation, and content generation workflows.

LLM Engineer

• lead the product marketing strategy for Cisco’s AI infrastructure portfolio
• connect technical capabilities to the business outcomes customers require for AI workloads and AI-native applications
• define the narrative, positioning, messaging, launch strategy, and sales enablement required to power secure, connected, AI-ready organizations
• partner with Product Management, Engineering, Sales, Corporate Marketing, Analyst Relations, and Communications
• translate sophisticated infrastructure innovation into market value
• drive cross-functional alignment
• equip the business to win with enterprise buyers and executives

Director of Product Marketing Strategy – AI Infrastructure

• Own a product capability end to end, from architectural decisions through production outcomes, cutting through every layer: entry points, orchestration, intelligence, tools, data, and observability
• Your work directly shapes how Augeo's clients engage tens of millions of users
• Build and ship mission-critical AI agents that drive revenue growth across finance, healthcare, telecom, and commerce
• Define how AI agents are built at Augeo
• Drive the Agent Development Life Cycle (ADLC) from initial pilot through production iteration
• Build the shared platform: agent orchestration, agentic workflows, ML models, and data infrastructure
• Work across Augeo with engineers, product leads, and end users to understand needs, close feedback loops, and ensure the platform compounds with every interaction
• Shape the team and participate in hiring

AI Architect

• Provide strategic leadership and direction to 2 to 3 small, senior-caliber engineering teams
• Guide teams in understanding client needs, setting project goals, and ensuring successful implementation
• Manage 2–3 high-performing implementation teams of senior engineers
• Support the company’s rapid growth as we double our client base
• Drive delivery against the product roadmap
• Champion collaborations with TA and existing external sourcing vendors
• Refine onboarding methodologies
• Engage with organizational leaders to co-design and champion career progression frameworks
• Mentor an Engineering Talent Manager
• Dive deeper into analytics and connect the dots between talent metrics and broader business objectives
• Champion an AI-first engineering culture
• Facilitate the creation and management of data and information repositories
• Foster an environment of proactive domain-specific knowledge sharing among team members
• Pioneer refinements in delivery processes
• Advocate for and pursue expertise in advanced talent management strategies
• Maintain regular and reliable attendance

Senior Manager, AI Engineering

Machine Learning Engineer

• Collaborate closely with ML Scientists to productize and scale ML models, from experimentation to robust production systems
• Design, build, and own large-scale, distributed machine learning systems for training, deployment, inference, and monitoring
• Lead design discussions and architecture reviews; drive high-impact engineering decisions for ML platforms and applications
• Mentor and coach junior engineers and peers on best practices in ML engineering, system design, and code quality
• Develop and maintain reusable components, libraries, and tools to accelerate ML development lifecycle
• Proactively identify areas for improvement in model performance, pipeline efficiency, data quality, or platform capabilities
• Ensure scalability, observability, and fault-tolerance across all components of the ML stack
• Promote engineering excellence by advocating for best practices in testing, CI/CD, infrastructure-as-code, and monitoring
• Partner with stakeholders across Data Engineering, Product, Marketing, and Platform teams to align solutions with business goals
• Stay up to date on advancements in MLOps, ML frameworks, distributed systems, and apply learnings to improve systems and processes

Senior Machine Learning Engineer

• Design, build, and operate end‑to‑end AI/ML solutions (data pipelines, models, services, and tools) for diagnostics, prognostics, and test analytics.
• Implement production‑grade ML pipelines on platforms such as Azure and Databricks, covering data ingestion, feature engineering, training, evaluation, and inference for batch and streaming workloads.
• Develop and maintain robust, observable ML services and internal tools that make complex vehicle and field data easy to use for engineers and technical stakeholders.
• Apply practical ML and statistical methods (e.g., tree‑based models, time‑series and anomaly detection, deep learning where appropriate) with a focus on reliability, explainability, and impact.
• Own model and data observability in production, including metrics, dashboards, alerts, and remediation workflows for drift, data quality, and performance regressions.
• Partner with data engineering to define and use industrialized and vectorized data products that support search, RAG, and analytics at scale.
• Review designs and code, mentor AI/ML practitioners, and help set high standards for testing, logging, deployment, and documentation.
• Collaborate with diagnostics/prognostics SMEs, validation, safety, and program teams to prioritize work, define success metrics, and embed solutions in day‑to‑day engineering workflows.

Staff Artificial Intelligence Machine Learning Engineer

• Write and ship production AI code daily — you are an active contributor.
• Architect agentic AI workflows using LangGraph, Temporal, Pydantic — stateful, multi-agent workflows built for enterprise scale and reliability.
• Own AI observability via LangFuse: tracing, prompt versioning, evaluation, and performance benchmarking across all model interactions.
• Set AI engineering standards for agent design patterns, RAG, prompt management, context optimization, and tool-calling strategies.
• Partner with product and platform teams to deliver AI architectures that meet enterprise SLA, security, and compliance requirements.
• Evaluate and adopt emerging tooling — benchmarking LLM providers, orchestration frameworks, and agentic stack improvements.
• Mentor engineers as a natural extension of your work — sharing knowledge through code reviews, pairing sessions, and design discussions, not through management overhead.
• Represent Acquia's AI capabilities in customer architectural reviews, technical discovery, and roadmap conversations.

• Work on the AI Platform team focusing on scalable and efficient pipelines for model training, evaluation, and data processing
• Build and evolve core libraries used by AI scientists to develop, launch, and monitor AI products
• Optimize GPU and CPU efficiency and data throughput of large-scale foundation models
• Ensure Artera’s observability infrastructure provides a clear picture of model performance optimization

Machine Learning Engineer – Platform

• Source relevant data from various channels, including databases, online sources, and internal systems, essential for training AI models.
• Process raw data to make it suitable for use in machine learning, which includes cleaning, normalizing, and segmenting the data.
• Create machine learning models using appropriate algorithms and techniques to solve specific business problems or enhance application capabilities.
• Rigorously test AI models to ensure their accuracy, reliability, and robustness, and validate them against predefined metrics.
• Seamlessly integrate AI models into existing software applications, employing API calls or embedded code for smooth functionality.
• Continuously optimize AI systems for better speed, efficiency, and accuracy, ensuring they meet the required performance standards.
• Identify and fix bugs or issues in AI models and their integration into applications, ensuring smooth operation.
• Keep abreast of the latest advancements in AI, machine learning, and related technologies to incorporate cutting-edge solutions.
• Work alongside other professionals, including AI Engineers, data scientists, software developers, and project managers, to achieve cohesive project goals.
• Create detailed documentation for AI systems and their architectures, aiding in maintenance, updates, and knowledge transfer.
• Utilize AI to improve the user interface and experience of applications, making them more intuitive and responsive.
• Adhere to ethical guidelines and standards in AI development, ensuring fairness, privacy, and transparency.
• Conduct in-depth analysis of AI systems' performance, identifying areas for improvement and optimization.
• Utilize cloud computing platforms for efficient AI model development, testing, and deployment.
• Incorporate user and stakeholder feedback into AI system development to continuously improve its effectiveness and usability.
• Serve as a bridge between technical and non-technical teams, facilitating the integration of AI into various business processes and ensuring alignment with business objectives.
• Partner with key stakeholders, including the business unit leaders, Product, Data & Technology teams, to assist with their AI-related technical issues.
• Contribute to the team of AI professionals by using expertise to answer questions and sharing repeatable design patterns with less experienced teammates, enhancing skillsets and competencies of team members, and sharing technical knowledge throughout the team.
• Participate in stakeholder reviews and design sessions.
• Provide data, reports, and information to management as needed.
• Drive strong communications, partnerships, and stakeholder management with senior leaders, functional managers, and staff.
• Perform other duties as assigned.

• This job is responsible for providing leadership, technical direction and oversight to a team delivering technology solutions.
• Key responsibilities of the job are to provide oversight of the design, implementation, and maintenance of complex computer programs, align technical solutions to business objectives, and ensure that coding practices/quality comply with software development standards.
• Job expectations include conducting multiple software implementations and applying both depth and breadth in knowledge of several technical competencies.
• This role is for a Feature Lead in the Generative AI Core Engineering Team, responsible for designing and delivering high-performance, scalable, and secure APIs to support Generative AI workloads.
• The Feature Lead will collaborate with cross-functional teams to ensure solutions meet business objectives while adhering to technical standards.

Feature Lead – Technology, GenAI Platform

• Extend, optimize, and maintain core data models that support customer-facing reports, machine learning, and generative AI workloads.
• Build and operationalize ML and AI workflows that streamline operations, reduce manual effort, and improve customer and business outcomes.
• Partner with engineering, product, and analytics teams to deliver seamless integrations and customer-facing data products.
• Implement data quality, observability, and governance frameworks to ensure reliable, well-managed data at scale.
• Document data flows, integration contracts, and operational runbooks to support efficient scaling and handoff.

Staff Data & Machine Learning Engineer

• Architect and integrate hybrid AI systems.
• Design and deploy scalable AI architectures.
• Lead the full AI/ML lifecycle.
• Engineer event-driven data pipelines and feature stores.
• Ensure Responsible AI practices.
• Implement and maintain MLOps pipelines.
• Transition R&D prototypes into production.
• Provide technical leadership and mentorship.
• Collaborate across engineering, data, and modeling teams.
• Support proposal and solution development.

Distinguished AI/ML Engineering Lead

• Architect and integrate hybrid AI systems that combine traditional machine learning, deep learning, large language models (LLMs), and retrieval-augmented generation (RAG) pipelines.
• Design and deploy scalable AI architectures including APIs, microservices, and model-serving frameworks that integrate seamlessly with analytic, simulation, or operational systems.
• Lead the full AI/ML lifecycle — from data ingestion and feature engineering through training, deployment, and sustainment within secure DoD environments (IL5/IL6, ATO, GovCloud).
• Engineer event-driven data pipelines and feature stores for both structured and unstructured data, including text, imagery, and simulation outputs.
• Ensure Responsible AI practices by embedding traceability, explainability, and confidence scoring into deployed systems.
• Implement and maintain MLOps pipelines (MLflow, Kubeflow, Airflow, Docker/Kubernetes) to support continuous integration, retraining, and drift detection.
• Transition R&D prototypes into production, optimizing for mission constraints such as limited compute, edge environments, or disconnected operations.
• Provide technical leadership and mentorship, setting standards for model quality, architectural design, and ethical AI deployment across programs.
• Collaborate across engineering, data, and modeling teams to unify FTI’s AI portfolio, ensuring interoperability and reuse across mission systems.
• Support proposal and solution development, providing technical inputs for AI/ML architectures, data strategies, and Responsible AI assurance frameworks.

Lead AI/ML Engineer

• Design, develop, and deploy AI/ML models and pipelines that meet mission and performance objectives.
• Build, train, and fine-tune models using frameworks such as PyTorch, TensorFlow, scikit-learn, Hugging Face, and LangChain.
• Develop and operationalize MLOps pipelines (MLflow, Kubeflow, DVC, or custom training/inference orchestration).
• Implement and optimize vector databases (Milvus, Pinecone, Chroma, FAISS) and retrieval architectures (RAG, graph, hybrid).
• Write clean, efficient Python code for data ingestion, feature engineering, embeddings, and inference services.
• Experiment with fine-tuning and optimization of LLMs and task-specific models (LoRA, QLoRA, PEFT).
• Contribute to agent-based applications using frameworks like LangGraph, AutoGen, CrewAI, or DSPy.
• Integrate AI services into real-world systems via APIs, event-driven workflows, or UI copilots.
• Collaborate with data engineers, software developers, and mission analysts to ensure AI models are production-ready and aligned with customer needs.
• Participate in peer reviews, contribute to shared repositories, and document models and experiments for reproducibility.

AI/ML Engineer

AI Research Scientist

• Engage in relevant research and development (R&D) of AI systems, models, and advanced machine learning algorithms that augment physics-driven modeling and simulation systems
• Explore and implement AI-powered simulation tooling in support of AI workflows through reinforcement learning, multi-agent systems, and hybrid modeling approaches
• Collaborate with research, engineering, and product teams to build AI-powered solutions that meet mission-critical modeling and decision-support needs
• Engage in and support the drafting and review of conference and journal articles and presentations, sharing advances with both internal stakeholders and the wider research community.
• Contribute content to technical invention disclosures, including associated narrative, graphics, and engagements in support of patent development
• Perform additional responsibilities (no more than 10% of duties) in support of the company’s technology and product development initiatives.

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Staff AI Engineer

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Staff AI Engineer

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AI Engineer

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Distinguished AI/ML Engineering Lead

Lead AI/ML Engineer

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