Principal Engineer – AI Specialist

Role Overview

• 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.

Tech Stack

• Cloud
• Distributed Systems
• Docker
• Grafana
• JavaScript
• JMeter
• Kafka
• Kubernetes
• OpenShift
• Prometheus
• Python
• SDLC
• Spark
• Terraform
• Go

Benefits

• Flexible work arrangements
• Professional development opportunities