First American - Senior Artificial Intelligence Engineer - LLM

Role : Senior AI_engineer

Mission / Position Statement :

We are hiring for great (Senior) AI Engineer who are open to learning the tools we use and building the AI Platform at First American.

What we are looking for Requirements are :

1. Candidate must have in-depth experience with at least one major cloud provider (AWS, GCP, Azure). AWS experience is optimal.

2. Experience with Real-World Application of AI such as document parsing (LLMs are optimal), recommendation engines, image classifications.

3. Worked with AI toolsets such as SageMaker, Vertex, Foundry or DataBricks.

4. Developed model deployment, experimentation and evaluation pipelines.

5. Experience presenting AI findings and use cases to leadership and non-technical audiences list end

Nice-to-haves are :

1. Experience with Agentic AI tooling such as : LanGraph, Bedrock Agents, Swarm, CrewAI.

2. Experience working with Analytics and Big Data teams, including tools such as : Hadoop and Spark.

3. Experience fine tuning an LLM, developing a RAG system, and prompt chaining.

4. Experience in building a Human in the Loop machine learning system

• Has a bias towards action; follows the MVP concept of ship and iterate
• Perseveres in the face of setbacks; "scrappy", ability to finish
• Communicates well.
• Effectively collaborates.
• Strong sense of empathy; raises hand when stuck, patient / polite with non-developers
• Self-Learner and flexible; we need engineers who can go off and learn a new concept with little handholding.
• Someone who is 'Married to the goal and not the process' (as there are many ways to solve a problem).
• Coachable; takes feedback well and can incorporate this into areas to improve
• Cloud platform & SDLC automation engineering; strong understanding of infrastructure technologies and software delivery automation and platforms (ex. Kubernetes, GitLab, GitHub). Understanding of distributed systems design and implementation. Experience with integration patterns.
• Problem decomposition; simplicity over complexity, can explains tradeoffs
• Systems design; prioritizes maintainable, observable and fault tolerant

(ref : hirist.tech)