AI Engineer

• Bring novel ideas and the content of scientific papers into high-performing and robust scientific code.
• Lead the ideation, benchmarking, and execution of complex datasets and ML models, ensuring seamless integration into our large-scale simulation frameworks.
• Drive software through the entire product lifecycle—from foundational research and implementation to launch and long-term support—ensuring technical excellence at every stage.

ML Research Engineer, AI for Life Sciences

• Produce a high volume of content across formats - short-form video, long-form YouTube, static and video ads, LinkedIn posts, written pieces, landing page and email creative - using AI tooling to multiply your output well beyond what a traditional content team can match.
• Build the factory: repeatable systems and workflows (AI tooling, templates, freelancers, repurposing pipelines) so content scales without linearly scaling headcount.
• Run and optimize paid ads directly off the content you create (Meta, LinkedIn, Reddit, and beyond) - launch, test, read the data, iterate, kill losers, scale winners.
• Turn our raw material - product stories, customer wins, founder POV, data insights - into a constant stream of native, on-brand content.
• Move fast and ship daily, treating volume plus iteration speed as the advantage.

AI Content Lead

• Deliver agentic AI solutions in production.
• Design, build, and ship AI agents for a partner business domain using the team’s internal platform and reusable components — owning the solution from first workshop to production operation.
• Partner deeply with the business. Embed with business organizations to understand their workflows, data, and operational touchpoints; identify where agentic AI delivers measurable improvement; and translate domain knowledge into technical designs.
• Build quickly and iterate. Compose solutions from platform capabilities rather than rebuilding from scratch; prototype fast, validate with users, then harden for production. Speed with rigor is the job.
• Apply state-of-the-art techniques. Use foundation models, LLM orchestration, retrieval, and transformer-based approaches for sequential and transactional data — choosing the simplest technique that ships.
• Own production quality. Build the evaluation, observability, and guardrail systems for everything you ship; ensure deployed agents meet the bank’s reliability, security, and compliance standards.
• Strengthen the platform. Contribute improvements, patterns, and reusable components back to the platform team so every delivery makes the next one faster.
• Communicate outcomes. Translate technical progress into presentable results — supporting partner stakeholder reviews and executive demos with clarity and credibility.

Lead Applied AI Engineer

• Architect and implement artificial intelligence and machine learning systems addressing diverse business challenges throughout the Bank
• Conduct rigorous data analysis and perform statistical evaluation
• Design experimental frameworks and develop algorithms leveraging both structured and unstructured data
• Create on-demand analytics to fully integrated software systems
• Stay apprised of and educate leaders on emerging AI trends, risks, and opportunities
• Create and maintain model documentation for transparency
• Research, test, and apply state-of-the-art generative AI models and/or solutions
• Develop agentic AI systems capable of autonomous reasoning, planning, and tool utilization
• Create evaluation frameworks to assess model performance
• Translate business questions into analytical problems with clear objectives
• Lead projects with limited supervision, influencing technical direction and standards
• Partner with cross-functional teams to understand business requirements and translate into technical solutions

Principal AI Engineer – AI Transformation

• Set technical direction across multiple services and teams, operating with broad autonomy
• Define the AI platform patterns other teams build on — agent orchestration, tooling, governance hooks, and evaluation surfaces
• Own reliability, testing, CI/CD, and operational standards across multiple teams
• Provide technical leadership across teams, and influence staffing and roadmap priorities
• Serve as a technical advisor and expert to teams, and embed within teams to deliver complex, high-value functionality
• Stay hands-on — design and build production systems in Python across the AI stack
• Define patterns and mentor engineers across teams, raising the technical bar

Principal AI Engineer

• Develop and implement AI/ML models and solutions to support healthcare data initiatives.
• Collaborate with cross-functional teams to ensure successful deployment of AI/ML technologies.
• Analyze and interpret complex healthcare data to drive actionable insights.
• Ensure compliance with healthcare regulations and guidelines regarding data usage.

• Partner with the Manager of Data Science & AI Engineering to scope and deliver AI-driven product features and internal tools.
• Design and implement machine learning and generative AI solutions using cloud services such as AWS Bedrock, SageMaker, and Amazon Q in QuickSight.
• Integrate AI services into web and application layers (e.g., via REST APIs, LangChain, or Bedrock SDK).
• Develop proof-of-concepts for natural language querying, document summarization, forecasting, and user experience enhancements using AI.
• Work with structured and unstructured data stored in AWS RDS, SQL Server, and other data sources.
• Collaborate with data engineering and analytics teams using tools like Power BI, QuickSight, and Python-based data pipelines.
• Ensure responsible AI design, including model monitoring, bias testing, and performance validation.
• Stay current with emerging technologies in AI (LLMs, vector databases, RAG architectures, and MLOps best practices).

• Lead the end-to-end system design, architectural framework, and product integration of Workday’s next generation of intelligent agents.
• Architect how foundational models are safely and reliably integrated into functional, production-grade software.
• Own the design, experimentation, and orchestration of complex agentic workflows, translating cutting-edge AI capabilities into enterprise-grade business value.
• Be a core champion for Responsible and Governed AI—architecting systems with strict guardrails for data privacy, predictability, and explainability.
• Balance high-level system design and hands-on execution, solving critical product constraints like latency, cost, and reliability.

• Contribute features, fixes, and optimizations upstream to vLLM/SGLang
• Implement and optimize inference‑runtime capabilities
• Profile and improve hot paths across layers
• Improve multi‑GPU inference performance and reliability
• Build and maintain performance and correctness regression tests
• Collaborate with model, platform, and SRE teams

Senior Software Engineer – AI Inference

• Design and build performance analysis tools and workflows for AI training and inference workloads.
• Understand how AI performance engineers work and translate their needs into scalable, intuitive tooling.
• Develop integrations between profiling infrastructure and AI frameworks and workflows.
• Collaborate with performance engineers, hardware architects, and software teams to ensure profiling capabilities align with real-world AI workloads.
• Identify performance bottlenecks in AI workloads and develop automated approaches to detect and diagnose them.

Senior Software Engineer, AI Performance Analysis

• Use AI for solving product problems in gaming and other interactive experiences.
• Build upon the latest research to create new models and invent new ways of applying AI to advance neural graphics and gaming.
• Create prototypes to demonstrate real-life applications of your ideas and to accelerate productization.
• Keep up with the latest AI/DL research and collaborate with diverse teams (both internal and external to NVIDIA), including AI/DL researchers, hardware architects, and software engineers.
• Participate in technology transfers to and from teams across NVIDIA.

Senior Machine Learning Graphics Engineer, AI for Experiences

• **System Design & Architecture:** Architect end-to-end Generative AI systems by operationalizing the 7-layer AI architecture (Data Sources, Preprocessing, Model Selection, Orchestration, Inference, Integration, and Application).
• **Model Selection & Tuning:** Evaluate and select cutting-edge commercial (e.g., GPT-4) and open-source models, and fine-tune models for domain-specific use cases.
• **LLMOps, Observability & Pipelines:** Establish LLMOps standards for model versioning and CI/CD. Implement foundational observability (OBS) layers using tools like Datadog, Splunk, or Prometheus to monitor system health, API latency, and basic application metrics.
• **Integration & Data Protection:** Integrate AI solutions with existing APIs while enforcing core data protection measures, including Role-Based Access Control (RBAC), data encryption in transit, and basic PII (Personally Identifiable Information) masking to manage hallucinations and adversarial attacks.
• **Strategic Leadership:** Collaborate with stakeholders to map business challenges to AI solutions and establish AI governance frameworks.
• **Client Consulting:** Act as the primary onshore technical liaison, facilitating client workshops, requirements gathering, and translating business pain points into technical AI blueprints.

Gen AI Enterprise Architect – AVP

• Develop and implement AI-driven solutions that enhance and scale AI adoption across Navy Federal Credit Union.
• Lead the design and implementation of cutting-edge AI systems using large language models to solve complex business problems.
• Collaborate with ETS and Business partners to build and drive AI solutions.
• Provide delivery and ongoing support for data science, advanced analytics, and augmented intelligence technologies.
• Exhibit excellent problem-solving skills and demonstrate strong leadership qualities with a proven track record of delivering measurable outcomes.

Principal AI Engineer – Agentic AI

AI Research Scientist

• Contribute to ongoing research projects and help define new research directions
• Develop internal prototypes to test and evaluate methods relevant to embodied AI assurance
• Document research findings in the form of reports, presentations, and scientific publications

AI Research Internship

• Contribute to ongoing research projects and help define new research directions
• Engage with universities to maintain and strengthen ongoing and future research collaborations
• Develop internal prototypes to test and evaluate methods relevant to embodied AI assurance
• Publish scientific results and attend conferences to build new knowledge and share DNV’s work

AI Researcher

• Design, develop, and deploy production-grade applications leveraging various LLMs, context optimization, etc.
• Architect and implement sophisticated, multi-step and multi-agent workflows using frameworks like LangChain and LangGraph.
• Build and optimize RAG pipelines, including implementing and managing embeddings, vector databases, and advanced rerankers to enhance response quality and relevance.
• Use code generation applications (e.g. Replit, Cursor, Google AI Studio, Git Hub Copilot in Agent mode, etc.) to create full applications (including frontend and backend), generate tests, perform testing and integrate them in the core product without writing any code.
• Lead efforts in LLM fine-tuning (e.g., LoRA, QLoRA) for specific domain knowledge and tasks, and implement strategies for efficiency.
• Develop and refine advanced prompt engineering techniques to maximize model performance, consistency, and safety.
• Own end-to-end implementation from frontend to the backend.
• Expose AI/LLM functionality written in Python using Java services, leverage multi-threading capabilities in Java to augment AI/LLM functionality developed in Python
• Utilize AI-powered development tools (e.g., GitHub Copilot, etc.) to efficiently generate, refactor, and optimize high-quality code.
• Work closely with team leads managers, QA, product managers and team in the US (this will require the willingness to partially work US working hours)

• Build Navanta’s retrieval and verifications over data systems, with shown queries and citations for every answer
• Stand up self-hosted open-weight models serving and embeddings inside each bank’s environment or shared environments for Navanta; evolve RAG to a dedicated standard
• Design the MCP tool layer that exposes a small, audited set of read-only tools (metrics, documents, customer 360), eventually growing into read/write tools with heavy amounts of regulated, highly sensitive data
• Build and maintain the evaluation harness — golden-question regression, groundedness and retrieval metrics, explicit “I don’t know” behavior — and make it a release gate
• Implement LLM guardrails: PII redaction in prompts and context, prompt-injection defenses, and cost and row limits aligned to regulatory security expectations
• Partner with data teams so the model selects governed metrics from the semantic layer rather than improvising SQL
• Document model architecture, evaluation methodology, and guardrail controls to support customer security reviews and audit readiness
• Track latency, cost, and quality trade-offs across model versions and deployment configurations

Lead AI ML Engineer

• Set the Applied AI vision and roadmap: Define how AI will transform workflows across intake and enrollment, care delivery, revenue cycle management, and compliance. Partner with Engineering, Product, Clinical, Operations, and Analytics to prioritize the highest-leverage opportunities.
• Build the intelligence layer of Abby Care’s operating system: Architect and develop agentic systems that reason over complex information, interact with internal tools, and execute consequential healthcare workflows end to end.
• Design for safe and scalable autonomy: Establish human-in-the-loop and human-on-the-loop patterns based on risk, reversibility, confidence, and regulatory requirements, combining AI with deterministic systems where appropriate.
• Create shared AI foundations: Build reusable capabilities for orchestration, retrieval, knowledge representation, tool use, memory, structured outputs, evaluation, tracing, and feedback loops.
• Establish production and evaluation rigor: Define how Abby Care measures, monitors, and improves AI systems across quality, safety, reliability, latency, cost, and downstream operational outcomes.
• Build and lead the Applied AI function: Recruit and mentor engineers, set a high technical bar, embed AI engineers into domain teams, and remain hands-on in the most ambiguous and technically critical areas.

Tech Lead, Applied AI

• The Principal AI Search & Conversation Architect will be responsible for designing and leading the architecture of a next-generation conversational AI platform that replaces traditional search with intelligent dialogue.
• This role owns the systems that understand user intent, determine what information is needed to satisfy a request, retrieve data from multiple real-time providers, reason across competing options, and generate conversational responses that lead users from discovery to transaction.
• The successful candidate will architect the AI orchestration layer that coordinates multiple large language models, specialized agents, retrieval systems, recommendation engines, and external APIs into a single seamless conversational experience.
• Rather than returning lists of search results, the platform will assemble and rank information from ticketing providers, hotels, airlines, restaurants, transportation services, mapping platforms, weather services, and other third-party data sources, presenting recommendations through natural conversation.
• This role requires deep experience designing AI-native systems capable of planning multi-step tasks, maintaining conversational context, coordinating tool execution, ranking competing results, and continuously adapting recommendations based on user feedback throughout a conversation.
• The Principal Architect will define the technical vision, establish architectural standards, evaluate emerging AI technologies, mentor senior engineers, and make long-term decisions regarding LLM strategy, retrieval architecture, agent design, memory management, semantic search, vector databases, knowledge graphs, and AI evaluation frameworks.
• The position requires extensive experience building large-scale distributed systems, production AI platforms, retrieval-augmented generation (RAG), multi-agent orchestration, function calling, Model Context Protocol (MCP), recommendation systems, API orchestration, prompt architecture, conversation state management, personalization, and low-latency cloud infrastructure.
• The Principal AI Search & Conversation Architect will work closely with the company’s other Principal Architects responsible for backend infrastructure, payments, blockchain, messaging, mini-programs, and platform services to ensure the conversational platform integrates seamlessly across the entire ecosystem while maintaining exceptional scalability, reliability, performance, and user experience.

Principal AI Search, Conversation Architect