Senior Software Developer building and scaling Nasdaq's big data pipeline infrastructure. Collaborating with teams to design, implement, and optimize data lake solutions for global markets.
About the role
- Principal Engineer leading AI-driven systems and models at F5, specializing in advanced AI technologies and collaboration across teams.
Responsibilities
- Lead the design and deployment of advanced AI-driven systems and models, including GPT-based solutions (GPT-4.x, OpenAI APIs), reinforcement learning frameworks, and autonomous agentic workflows
- Develop intelligent agents capable of handling complex tasks, decision-making, and automating workflows within F5 products and platforms
- Enable generative AI capabilities, such as personalization, contextual understanding, natural language processing (NLP), and decision systems
- Research and integrate state-of-the-art AI techniques, including transformer models, GANs (Generative Adversarial Networks), and hybrid AI architectures
- Develop and optimize large-scale distributed AI infrastructure, ensuring fault tolerance, resilience, scalability, and performance in global workloads
- Implement advanced observability systems for AI applications, leveraging telemetry pipelines (e.g., OpenTelemetry, Prometheus) and ensuring data quality validation
- Create frameworks for real-time anomaly detection, predictive analytics, and failure recovery simulation in AI systems
- Build frameworks for automated data ingestion, transformation, and validation at scale across distributed systems (e.g., Kafka, Flink, Spark)
- Ensure robust CI/CD pipelines tailored for AI workflows, including validation, automation, and monitoring for model updates and deployments
- Design synthetic data generation tools for model benchmarking, stress testing, and performance analysis of high-volume data sets and queries
- Implement chaos engineering and resilience testing for AI-driven cloud environments (Kubernetes, Docker, Helm)
- Create best-in-class AI architecture roadmaps, ensuring alignment with organizational goals and the latest advancements in AI technology
- Partner with Product, Engineering, SRE, and DevOps teams to embed AI capabilities into the SDLC, promoting quality and efficiency at every stage
- Mentor engineering teams on AI development, distributed system reliability, and automation strategies, fostering innovation and collaboration across teams
- Investigate production issues, contributing to root cause analysis, remediation, and future-proofing AI systems.
Requirements
- 10+ years of hands-on experience in AI research, development, and deployment
- Proficiency in natural language processing (NLP), computer vision, predictive analytics, decision systems, and multi-agent frameworks
- Familiarity with cutting-edge AI techniques such as GANs (Generative Adversarial Networks), hybrid transformers, sequence modeling (RNNs, LSTMs), and unsupervised learning approaches
- Expertise in building multi-modal AI systems capable of handling text, images, audio, and structured data
- Proven experience in advanced AI tooling and platforms (e.g., Hugging Face Transformers, LangChain, DeepMind frameworks, AI interpretability tools, RASA conversational systems)
- Deep working knowledge of distributed systems architecture, including large-scale data pipelines (e.g., Kafka, Flink, Spark), data lakes (ClickHouse, Iceberg, S3), and storage optimization techniques
- Experience in MLOps practices, including model lifecycle management, scaling, retraining, and deployment in production environments
- Proven expertise in Kubernetes, OpenShift, Terraform, and Helm for automating AI system deployment and scaling across multi-cloud infrastructures
- Strong knowledge of fault tolerance, latency optimization, and large-scale AI infrastructure monitoring using Prometheus, Grafana, and Datadog
- Advanced experience with anomaly detection, predictive modeling, and telemetry validation applied to distributed AI systems
- Proficiency in Python, Go, JavaScript, or similar programming languages for creating robust AI workflows and solutions
- Experience with benchmarking tools (Locust, Gatling, JMeter) and frameworks for AI performance testing at scale
- Ability to design customized AI APIs and algorithms to optimize automation workflows
- Proven ability to mentor and lead teams of engineers, QA specialists, and developers in adopting advanced AI practices
- Strategic mindset to align technical solutions with business goals, incorporating cutting-edge AI advancements to solve complex challenges
- Excellent communication skills for engaging stakeholders, presenting on AI strategies, and fostering cross-functional collaboration.
Benefits
- Flexible work arrangements
- Professional development opportunities
Job title
Job type
Experience level
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Not specified
Degree requirement
Tech skills
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