Senior AI Engineer

Job Description

Job Description

Location: West Hollywood / Los Angeles, CA
Department: Enterprise Technology – Data Engineering & AI
Employment Type: Full-Time
Work Model: On-Site (5 days per week)

Applicants must be legally authorized to work in the United States. Visa sponsorship is not available for this role.

About the Organization

Our client is a sophisticated private family office and investment management organization serving a diverse portfolio of investments, foundations, philanthropic initiatives, and other activities. With a uniquely broad mandate, the organization oversees diverse assets and programs across multiple entities, manages hundreds of employees, and oversees significant annual expenditures ranging from grants and gifts to private investments and operational costs.

We 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

The ideal candidate 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.

Ideal Profile

We are particularly interested in very hands-on Senior/Staff-level AI/ML engineers who have actually shipped production RAG and agentic AI systems .

Strong Python, LLM, fine-tuning/evaluation, vector database, Kubernetes, MLflow/Ray, and production deployment experience are particularly important.

Candidates whose GenAI experience is primarily focused on research, traditional data science, or prototypes rather than production systems are less aligned with the scope of this position.