Sr. AI Pipeline Engineer

Overview

This is an incredible opportunity to be part of a company that has been at the forefront of AI and high-performance data storage innovation for over two decades. DataDirect Networks (DDN) is a global market leader renowned for powering many of the world's most demanding AI data centers, in industries ranging from life sciences and healthcare to financial services, autonomous cars, Government, academia, research and manufacturing.

  

"DDN's A3I solutions are transforming the landscape of AI infrastructure." – IDC 

  

“The real differentiator is DDN. I never hesitate to recommend DDN. DDN is the de facto name for AI Storage in high performance environments” - Marc Hamilton, VP, Solutions Architecture & Engineering | NVIDIA 

  

DDN is the global leader in AI and multi-cloud data management at scale. Our cutting-edge data intelligence platform is designed to accelerate AI workloads, enabling organizations to extract maximum value from their data. With a proven track record of performance, reliability, and scalability, DDN empowers businesses to tackle the most challenging AI and data-intensive workloads with confidence. 

  

Our success is driven by our unwavering commitment to innovation, customer-centricity, and a team of passionate professionals who bring their expertise and dedication to every project. This is a chance to make a significant impact at a company that is shaping the future of AI and data management. 

  

Our commitment to innovation, customer success, and market leadership makes this an exciting and rewarding role for a driven professional looking to make a lasting impact in the world of AI and data storage. 

Job Description

We're seeking a Sr. AI Pipeline Engineer to join our innovative team in developing and deploying cutting-edge GenAI and LLM-based applications across multiple industry verticals. This role requires someone who can transform conceptual whiteboard designs into production-ready code with speed and precision. The ideal candidate thrives in our dynamic, fast-paced environment where change is constant and ambiguity is embraced as an opportunity for creative problem-solving. You’ll work closely with product teams, data scientists, and enterprise customers to bring intelligent solutions to life and accelerate adoption of AI platforms.

 

Key Responsibilities

• Architect, develop, and deploy end-to-end AI workflows and data pipelines for GenAI applications
• Design AI pipeline use cases across a variety of enterprise applications
• Deliver technical presentations, demos, and workshops for internal teams and external audiences
• Transform broad requirements into tangible, customer-usable assets and conference-ready demonstrations
• Build demos and reference architectures that illustrate the capabilities of AI pipeline solutions
• Collaborate across teams to integrate AI solutions with DDN's data storage infrastructure
• Collaborate with enterprise customers to define and present AI pipeline use cases
• Stay current with emerging AI technologies, tools, and methodologies
• Apply responsible AI principles throughout the development lifecycle
• Participate in customer-facing technical discussions to understand requirements and showcase solutions
• Contribute to our continuous innovation culture through knowledge sharing and mentorship

Requirements

• Advanced degree with AI focus or 5+ years of equivalent experience delivering AI applications to Fortune 100 and high-tech customers
• Demonstrated ability to rapidly translate conceptual designs into production code for LLM-based applications
• Deep understanding of end-to-end AI workflows, data pipelines, and toolchain ecosystems including:
  • Orchestration frameworks (Airflow, Prefect, Flyte, MLFlow)
  • ML libraries (PyTorch, TensorFlow, JAX, FLAX)
  • Visualization tools (ggplot, matplotlib, seaborn)
  • Acceleration technologies (CUDA rapids, PTX, TensorRT)
  • Data processing frameworks (Spark, Snowflake)
  • LLM frameworks (VLLM, sglang, HuggingFace Transformers)
• Familiarity with observability tools such as Grafana and Splunk
• Proficiency with AI-assisted development tools (Cursor, GitHub Copilot, etc.)
• Knowledge of emerging AI concepts: agent-to-agent behavior, model context protocol, steering, and interpretability
• Strong multi-cloud fundamentals with understanding of containers, APIs, multi-tenancy, security, and data movement
• Experience implementing enterprise AI principles: responsible AI, privacy, governance, evaluations, and guardrails
• Ability to keep pace wit