Ai Engineer Jobs

About The Position

Requirements

• 7+ years of professional software engineering experience.
• 3+ years of experience developing AI, Machine Learning, or Generative AI solutions.
• Demonstrated experience architecting, developing, and deploying production-grade Generative AI and LLM-based solutions, including agentic workflows, intelligent agents, and enterprise tool integration frameworks.
• Strong software engineering fundamentals and expertise delivering cloud-native applications and services using containers, serverless architectures, and modern public cloud platforms.
• Experience building scalable distributed systems using asynchronous processing, event-driven architectures, durable messaging, and high-performance data access patterns.
• Hands-on experience developing RAG solutions, including embeddings, semantic search, knowledge grounding, context engineering, prompt optimization, and prompt lifecycle management.
• Experience implementing AI evaluation, testing, monitoring, and observability frameworks to measure model quality, reliability, performance, and safe operation in production.
• Strong API design and integration experience, including secure, reusable, scalable platform services for enterprise AI adoption.
• Technical leadership experience mentoring engineers, driving architectural decisions, influencing technology strategy, and collaborating with cross-functional stakeholders.
• Hands-on experience with enterprise-approved AI-assisted software development tools, with outputs validated through secure coding practices, peer review, and automated testing.
• Strong understanding of responsible AI principles, including data privacy, security, governance, resiliency, and risk management.
• Deep understanding of cybersecurity data sources, including endpoint, network, application, cloud, and system telemetry, and their use in security monitoring, analytics, and automation.
• Experience with SIEM platforms and cybersecurity analytics solutions to ingest, correlate, retrieve, and analyze large-scale security data.
• Strong understanding of cybersecurity operations, including threat detection, incident response, threat hunting, security analytics, and security automation.
• Ability to lead and influence geographically distributed teams through virtual collaboration and effective partnerships across engineering, security, and business organizations.
• Deep understanding of CI/CD tools and methodology, with production experience designing and implementing pipelines that support frequent, reliable releases.
• Strong software development and automation skills with demonstrated experience in Python, JavaScript/TypeScript, Rust, Go (Golang), Bash, and PowerShell.
• Ability to lead complex technical initiatives while remaining deeply hands-on.
• Demonstrated success designing and deploying enterprise-scale applications and platforms.
• Bachelor’s or master’s degree in computer science, software engineering, artificial intelligence, data science, cybersecurity, or a related discipline.

Nice To Haves

• Experience with LangGraph, Semantic Kernel, CrewAI, AutoGen, LangChain, or similar frameworks.
• Experience with Databricks, Snowflake, Spark, Kafka, Delta Lake, Iceberg, and Airflow.
• Experience with vector databases, semantic search, and enterprise RAG platforms.
• Experience implementing MLOps, LLMOps, AI observability, and evaluation frameworks.
• Knowledge of Responsible AI, data governance, and model risk management.
• Experience developing cybersecurity, analytics, or operational intelligence solutions.

Responsibilities

• Drive architecture and hands-on delivery of scalable, reliable agentic AI platforms for security workflows.
• Design and build production-grade AI systems, including agents, skills, memory patterns, guardrails, and tool-use orchestration.
• Architect retrieval and context-engineering approaches, including embeddings, semantic search, grounding, summarization, and prompt/version management.
• Engineer cloud-native AI services in AWS, Azure, and GCP using containers, serverless patterns, event-driven messaging, and distributed data stores.
• Optimize platform latency, throughput, scalability, caching, context efficiency, and cost controls.
• Build well-governed APIs and integrations connecting AI capabilities to security platforms, tools, and business processes.
• Establish evaluation, research, regression-testing, and observability frameworks to improve quality and agent behavior.
• Define engineering standards for reliability, security, and safe AI operation throughout the platform lifecycle.
• Mentor junior engineers and influence engineering direction through code reviews, architecture forums, and cross-team technical leadership.
• Use enterprise-authorized AI coding-assistance tools to improve code quality, delivery speed, and productivity; validate outputs through peer review, automated testing, and secure coding standards.
• Apply software development lifecycle toolchain knowledge, including authorized AI-assisted development and automation capabilities, to improve the value realized from automation.
• Resolve occasional Level 2–3 escalations supporting operational incidents.

Benefits

• Retirement savings plan (401K) with company match.
• Insurance coverage including basic life, medical, dental, vision, long-term disability, and optional additional coverage.
• Paid time off including vacation, sick leave, short-term disability, and family care responsibilities.
• Access to an Employee Assistance Program.
• Eligibility for annual performance-based incentive awards, excluding certain sales roles subject to sales incentive plans.
• Eligibility for certain tax-advantaged savings plans.
• Inclusive development opportunities.
• Flexible work-life support.
• Paid volunteer days.
• Employee networks.