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David Careers

Senior AI / ML Engineer

Posted today

ai talent nowMillbrae (CA)
Software DevelopersCustom Computer Programming Services

SENIORITY

Lead

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About the role

San Francisco, United States | Posted on 10/22/2026AI Talent Now, LLC is a powerhouse in direct-hire talent acquisition, connecting exceptional talent with industry-leading organizations across IT, Engineering, Financial Services & Fintech, and Manufacturing & Robotics. Headquartered in Atlanta, Georgia, we proudly serve clients and candidates nationwide.
Job Description: AI Talent Now job ZR 110
About this role: We're looking for a Senior AI/ML Engineer to build the AI systems layer behind this company's process intelligence platform. This is a full-time role for a senior applied AI engineer who cares about making AI behavior measurable, reliable, safe, and useful in production. Our dynamic client builds production AI systems on top of hosted frontier models from providers like OpenAI, Anthropic, etc. You will own the systems that turn messy enterprise data into structured process intelligence: retrieval, context construction, model/provider selection, structured extraction, evals, orchestration, quality loops, and monitoring.
Requirements
What we're looking for
Must-haves:
  • 4-6+ years building production software, ML systems, or applied AI systems
  • Production AI engineering: You have strong skills and experience building reliable, observable AI services and pipelines, including handling latency, cost, retries, failures, and debugging in production
  • Practical LLM experience: You have hands-on experience turning hosted LLM APIs into product behavior through structured outputs, retrieval and context construction, tool use, or multi-step workflows.
  • AI quality ownership: You can define what good means, build representative datasets and evaluations, diagnose failures in data or model behavior, and ship regression tests, guardrails, and fallback paths.
  • AI evaluation and guardrails: Experience with golden datasets, LLM-as-judge, human review, regression testing, launch gates, hallucination reduction, constraint enforcement, verification, and fallback paths.
  • Systems thinking: You can reason about pipelines end-to-end, from raw customer data to generated outputs and user-facing product behavior
  • Pragmatic mindset: You know when to refactor vs. ship, when to build vs. buy, and how to manage technical debt intentionally
  • Ownership mentality: You take end-to-end responsibility for features, from design through deployment and monitoring
  • Comfort with ambiguity: You can turn a fuzzy product goal into experiments, implementation, measurement, and shipped improvements
  • Strongly preferred
  • Customer-facing communication: Clear written and verbal communicator with users/customers
  • Retrieval and knowledge systems: Hands-on experience with embeddings, hybrid search, chunking, ranking, reranking, and assembling useful context from messy knowledge sources.
  • Agentic workflows: Experience making multi-step, tool-using AI systems reliable when plans, tools, or intermediate outputs can fail.
  • Document understanding: Experience extracting structured, trustworthy information from long or messy enterprise documents.
  • AI-assisted development: You already use tools like Claude Code, Cursor, Codex, Copilot, or similar to accelerate your work
Nice to have:
  • Process mining, task mining, or enterprise workflow experience
  • Multimodal inputs: Experience with documents, screenshots, video, transcripts, or mixed enterprise data sources
  • Go or backend service experience
  • Tech stack
  • Go, Python, PostgreSQL, Redis/Valkey, React 19 + TypeScript, GCP, Terraform, Docker, Datadog, GitHubAI stack
  • Hosted LLM APIs (OpenAI, Anthropic, Gemini), embeddings and retrieval, structured extraction, agent/tool-calling workflows, eval pipelines, and AI observability

Before you apply

Applying takes about a minute. These four things decide how fast it moves after that.

Your profile is current

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Two examples you can talk through

Not a portfolio — just two pieces of work where you can explain the decisions and what you'd change.

A number in mind

What you're on now and what would make you move. We negotiate better when we know both.

Your notice period

Employers plan around it, and it's the question that stalls offers most often.

Once you apply, someone reads it and calls you before anything reaches the employer — usually within two working days.

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