Senior AI Engineer

hop; Los Angeles (CA); On-site; USD 180,000 - 240,000; Full time

Benefits offered by this job

On-site in West Hollywood

Base + bonus

Job summary

hop in Los Angeles is seeking a Senior AI Engineer to architect production-grade LLM agents and RAG pipelines within Enterprise Technology Data Engineering & AI. You will own full ML lifecycle from data prep to GPU-scale deployment, building GenAI applications for multiple business functions.

Role requires expert Python/SQL, experience with LangChain/LlamaIndex, Docker/Kubernetes, MLflow, vector databases, BigQuery/Redshift, and strong communication to guide cross-functional teams.

Qualifications

• Bachelor’s or Master’s in Computer Science, Data Science, or equivalent experience.
• 7+ years designing and shipping ML/AI applications, including 2+ years with LLMs or Generative AI.
• Proven delivery of RAG or agentic systems in production (LangChain, LlamaIndex, n8n, or custom).
• Expert-level Python and SQL; Spark, distributed data-processing, performance-tuning.
• Fine-tuning foundation models; MLflow, Ray/KubeRay, vector databases.
• Experience with BigQuery/Redshift, Parquet/Avro, and streaming ingestions (Airbyte, Kafka).
• Production experience with Docker, Kubernetes, Helm, and Git-based CI/CD.
• Clear communicator; able to lead requirements and influence cross-functional teams.

Responsibilities

• Build & Ship Gen AI Apps: design, prototype, and build GenAI solutions, RAG pipelines, and agents for multiple functions.
• Agent Workflow Pipelines: orchestrate multi-step pipelines with LLM prompts, external APIs, and human-in-the-loop escalations.
• End-to-End ML Lifecycle: data prep → feature engineering → fine-tuning → evaluation → MLflow registry and drift alerts.
• Data & Storage Architecture: ingest from BigQuery, store embeddings in vector DBs, govern with OpenMetadata and ACLs.
• Scalable Deployment & Ops: Docker packaging, Kubernetes deployment, GPU scheduling, CI/CD with GitHub Actions.
• Observability & Compliance: instrument metrics/logs, run A/B/shadow tests, embed security and cost-guardrails.
• Lead & Mentor: translate ideas into roadmaps, perform build-vs-buy analyses, set standards, coach peers.

Skills

Python

SQL

LLMs

LangChain

LlamaIndex

Ray/KubeRay

Docker

Kubernetes

MLflow

BigQuery

Redshift

Spark

Vector DBs

OpenMetadata

CI/CD

Education

Bachelor’s or Master’s in Computer Science or Data Science

Tools

n8n

Docker

Kubernetes

Helm

GitHub Actions

Ray

KubeRay

OpenMetadata

Job description

Job Description

Our client is a family office management company serving investments, foundations, and activities of a prominent family. With a broad mandate, their organization oversees diverse assets and programs, including multiple foundations and institutes. Across their entities, they manage hundreds of employees and oversee significant annual expenditures, ranging from grants and gifts to private investments and operational costs.

They are seeking a highly motivated, innovative, and collaborative Technology staff member to serve as the Senior AI Engineer. The selected candidate will be a member of the Enterprise Technology Data Engineering & AI team, playing a pivotal role in driving innovation across the organization.

Summary

You will architect and develop production-grade LLM agents and RAG pipelines, steer the full ML lifecycle from data prep to GPU-scaled deployment, and weave together modern tools and technologies into a secure, cost-aware platform. If you thrive on turning ambiguous ideas into high-impact GenAI products and mentoring others to do the same, this is your playground.

Responsibilities

• Build & Ship Gen AI Apps: Design, prototype, and build GenAI solutions, RAG document pipelines, and task-specific agents to support multiple business functions using tools such as LangChain/LlamaIndex, micro-services, Ray/KubeRay.
• Agent Workflow Pipelines: Design and orchestrate multi-step agent pipelines, integrating LLM prompts, external APIs, and human-in-the-loop escalations.
• End-to-End ML Lifecycle: Own requirements → data prep → feature engineering → classical ML or LLM fine-tuning (LoRA, PEFT, RLHF) → offline/online evaluation → MLflow registry, with automated drift and quality alerts.
• Data & Storage Architecture: Ingest from BigQuery, object-store lakes (Parquet, Avro); generate embeddings and persist to vector DBs (Qdrant/PgVector); enforce governance via OpenMetadata and column-level ACLs.
• Scalable Deployment & Ops: Package with Docker, helm-deploy on Kubernetes; implement GPU scheduling, autoscaling, blue-green rollouts, and cost telemetry via Prometheus/Grafana; automate CI/CD in GitHub Actions.
• Observability & Compliance: Instrument tracking, metrics, and structured logs; run A/B or shadow tests; embed security, privacy, and cost-guardrails in every pipeline.
• Lead & Mentor: Translate ambiguous business ideas into executable roadmaps, run build-vs-buy analysis, set code standards, and coach peers on agentic patterns and ethical AI.

Requirements

• Bachelor’s or Master’s in Computer Science, Data Science, or equivalent experience.
• 7+ years designing and shipping ML/AI applications, including 2+ years with LLMs or Generative AI.
• Demonstrated delivery of RAG or agentic systems in production (e.g. LangChain, LlamaIndex, n8n, or custom).
• Expert-level Python and SQL; strong Spark, distributed data-processing, and performance-tuning skills.
• Hands-on fine-tuning of foundation models; comfort with MLflow, Ray/KubeRay, and vector databases.
• Deep familiarity with cloud warehouses (BigQuery, Redshift), lake formats (Parquet, Avro), and streaming/ingestion tools (e.g. Airbyte, Kafka/Pub-Sub).
• Production experience with Docker, Kubernetes, Helm, and Git-based CI/CD pipelines.
• Clear communicator able to gather requirements, set technical direction, and influence cross-functional teams.

Additional Details

• Only open to U.S. Citizens or Green Card holders.
• The role is in-office (LA) - West Hollywood.
• Compensation includes a strong base + bonus (no equity, as they’re private).

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.