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).

Requirements

Must-haves

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.
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.
Only open to U.S. Citizens or Green Card holders.
The role is in-office (LA) - West Hollywood.

Nice-to-haves

Compensation includes a strong base + bonus (no equity, as they’re private).

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aillmmldockerkubernetesci/cdpythonsql

Platform: lever
Effort: quick