Senior AI Engineer

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  "active_status": "active"
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    "raw": "Los Angeles",
    "city": "Los Angeles",
    "region": "CA",
    "country": "United States",
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{
  "lists": [
    {
      "text": "Responsibilities:",
      "content": "\n<li><strong>Build &amp; Ship Gen AI Apps:</strong> 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.</li>\n<li><strong>Agent Workflow Pipelines:</strong> Design and orchestrate multi-step agent pipelines, integrating LLM prompts, external APIs, and human-in-the-loop escalations.</li>\n<li><strong>End-to-End ML Lifecycle:</strong> 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.</li>\n<li><strong>Data &amp; Storage Architecture:</strong> 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.</li>\n<li><strong>Scalable Deployment &amp; Ops:</strong> 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.</li>\n<li><strong>Observability &amp; Compliance:</strong> Instrument tracking, metrics, and structured logs; run A/B or shadow tests; embed security, privacy, and cost-guardrails in every pipeline.</li>\n<li><strong>Lead &amp; Mentor:</strong> Translate ambiguous business ideas into executable roadmaps, run build-vs-buy analysis, set code standards, and coach peers on agentic patterns and ethical AI.</li>\n"
    },
    {
      "text": "Requirements:",
      "content": "\n<li>Bachelor’s or Master’s in Computer Science, Data Science, or equivalent experience.</li>\n<li>7+ years designing and shipping ML/AI applications, including 2+ years with LLMs or Generative AI.</li>\n<li>Demonstrated delivery of RAG or agentic systems in production (e.g. LangChain, LlamaIndex, n8n, or custom).</li>\n<li>Expert-level Python and SQL; strong Spark, distributed data-processing, and performance-tuning skills.</li>\n<li>Hands-on fine-tuning of foundation models; comfort with MLflow, Ray/KubeRay, and vector databases.</li>\n<li>Deep familiarity with cloud warehouses (BigQuery, Redshift), lake formats (Parquet, Avro), and streaming/ingestion tools (e.g. Airbyte, Kafka/Pub-Sub).</li>\n<li>Production experience with Docker, Kubernetes, Helm, and Git-based CI/CD pipelines.</li>\n<li>Clear communicator able to gather requirements, set technical direction, and influence cross-functional teams.</li>\n"
    },
    {
      "text": "Additional Details:",
      "content": "\n<li style=\"list-style-type: revert;\">Only open to U.S. Citizens or Green Card holders.</li>\n<li>The role is in-office (LA) - West Hollywood.</li>\n<li>Compensation includes a strong base + bonus (no equity, as they’re private).</li>\n"
    }
  ],
  "country": "US",
  "createdAt": 1774555811485,
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  "categories": {
    "team": "Artificial Intelligence",
    "location": "Los Angeles",
    "commitment": "Full- Time",
    "department": "Engineering",
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      "Los Angeles"
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