Applied AI Engineer - Bay Area

Redis

$175K — $263K *
Information Technology
5 - 7 years of experience
Job Overview by Ladders

Qualifications

  • 5+ years of professional programming experience in Python or similar language.
  • 2+ years of experience in machine learning (MLOps) or AI/ML model utilization.
  • Eagerness to learn and grow; admits when knowledge gaps exist.
  • Passion for building libraries and systems relevant to AI.
  • Ability to self-learn and navigate unfamiliar codebases.
  • Ownership mentality and willingness to go above and beyond.
  • Interest in working on open-source or widely accessible projects.

Responsibilities

  • Assist customers in developing AI applications with Redis.
  • Develop and maintain projects, primarily in Python, with potential for other languages.
  • Provide consultation during sales calls, applying AI expertise.
  • Participate in sales meetings and enablement as a subject matter expert.
  • Contribute to core Redis AI technology and AI ecosystem integrations.

Benefits

  • Unlimited time off for work-life balance.
  • Learning and development opportunities to foster growth.
  • Comprehensive health and wellness benefits to support wellbeing.
  • 401(k) plan for retirement saving options.
Full Job Description
Why would you love this job?

We are seeking a highly skilled and motivated Senior Applied AI Engineer to join our small but efficient Applied AI Engineering Team. As a part of this team, you will play a crucial role in helping our customers succeed with AI, writing demo applications, delving deep in core Redis AI technology, and contributing to AI ecosystem integrations. This position requires a strong focus on customer engineering (80%) and some software development (20%).

What you'll do:
• Help our customers succeed in building AI applications with Redis. Our customers are building chatbots, RAG applications, and AI agents, and they need your expertise.
• Develop and maintain projects primarily written in Python, with possible involvement in JS, C/C++, Go, or Java depending on your skillset.
• Occasionally provide consultation during sales calls, leveraging your expertise in AI.
• Occasionally participate in sales meetings, enablement, and other events as a subject matter expert.

What will you need to have?
• Minimum of 5 years of professional programming experience in Python or a comparable language.
• At least 2 years of experience working on ML systems (MLOps), or utilizing AI/ML models.
• Willingness to admit when you don't know something and actively seek opportunities for learning and growth.
• Passion for building libraries and systems
• Ability to independently learn and navigate new codebases
• Ownership mentality, going above and beyond what is required because you genuinely enjoy this type of work.
• Excitement about working on projects that are Open Source and/or accessible to the world.
• Enjoyment of working in a fast-paced environment with multiple projects to juggle simultaneously.

Extra nice if you have:
• Prior contributions to projects like Langchain, LlamaIndex, Semantic Kernel, Redis-related projects, or MLOps systems for feature orchestration like Feast.
• Knowledge of patterns such as RAG, semantic caching, feature storage and embedding generation.
• A strong passion for Vector Databases, LLMs, or AI systems in general.

The estimated gross base annual salary range for this role is $175,822 - $263,822 per year in New York, California, Washington, Colorado, and Rhode Island. Actual compensation may vary and is dependent on various factors, including a candidate's work location, qualifications, experience, and competencies. Base annual salary is one component of Redis' total compensation and competitive benefits package, which may include 401(k), unlimited time off, learning and development opportunities, and comprehensive health and wellness benefits. This role may include discretionary bonuses, stock options, commuter benefits based on location, or a commission plan. Salary history is not used in compensation package decisions. Redis utilizes market pay data to determine compensation, so posted compensation ranges are subject to change as new market data becomes available.

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