Senior ML & GenAI Engineer

This is a highly confidential search and will require a signed NDA to disclose the company name. The role is based in Los Angeles, CA.

About Eleven Recruiting

We are a specialized technology staffing agency supporting professional and financial services companies. Why do we stand out in technology staffing? We listen and act as advisors for our candidates on how they can best add value, find interesting projects, and pave a path for career advancement. We advocate for best pay, diversity in tech, and best job-fit for every candidate we place.

Our client is a management firm that runs the investments, initiatives, and operations for a visionary collective committed to shaping the future. Its reach is vast-overseeing diverse assets, funding transformative projects, and building programs that redefine science, philanthropy, and innovation. With a team of over 800 employees, this organization not only manages resources, but also drives meaningful change. From advancing groundbreaking ocean research to revolutionizing grant and investment strategies, every decision is purposeful.

This firm is seeking a Senior ML & GenAI Engineer to architect and ship 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 :

• 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.
• Design and orchestrate multi-step agent pipelines in n8n (or similar low-code orchestrators), integrating LLM prompts, external APIs, and human-in-the-loop escalations.
• 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.
• Ingest from BigQuery, Redshift, object-store lakes (Parquet, Avro); generate embeddings and persist to vector DBs (Qdrant / PgVector); enforce governance via OpenMetadata and column-level ACLs.
• 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.
• Instrument tracing, metrics, and structured logs; run A / B or shadow tests; embed security, privacy, and cost-guardrails in every pipeline.
• Translate ambiguous business ideas into executable roadmaps, run build-vs-buy analyses, set code standards, and coach peers on agentic patterns and ethical AI.

Qualifications :