Staff AI Engineer

Meeru AI Inc is building an

AI and intelligence layer

where outputs must be grounded, traceable to verified data, and evaluated for hallucination control. This Staff AI Engineer role is a hands-on, senior position that sets the technical direction for ML, LLM, and agent systems while partnering with evaluation to make quality measurable. The work is delivered

onsite in Pleasanton, CA , supporting deployments that keep customer data in their environment.

What you will own

Own the

AI and intelligence layer

and the discipline that keeps it honest.

Own the

machine-learning models

that learn each customer’s patterns.

Own the

LLM and agentic systems

that produce grounded natural-language output.

Build

per-customer models

with strict anti-leakage discipline so models do not expose the signals they detect.

Set a high bar for

evaluation, grounding, and hallucination control

across the layer.

Provide

technical direction

for the AI layer.

Partner closely with the

evaluation engineer

to make quality measurable.

Turn

user feedback

into durable improvements over time.

What you bring

Staff-level applied ML/AI

experience delivering models in production.

Strong classical ML with hands-on depth in

time-series modeling ,

anomaly and outlier detection , and

gradient-boosted models

such as

LightGBM

or

XGBoost , using

Python ,

scikit-learn , and

pandas

or

Polars .

Modern LLM and agent patterns, including

retrieval-augmented generation (RAG) ,

tool-using agents ,

structured (schema-validated) output , and orchestration with

LangGraph

or a similar framework.

Rigor around

evaluation, grounding, and hallucination control , including measuring whether outputs are faithful to source data and building guardrails that prevent unsupported statements from reaching users.

Clear discipline on

data leakage

and honest evaluation, designing features, splits, and validation so training and scoring reflect what is available in real use.

Clear thinking about what ML can and cannot claim, including communicating to non-ML colleagues where outputs are

evidence

vs.

signals .

Real software engineering : tested, production-grade Python services; comfort with

SQL

against

Snowflake

or

PostgreSQL ; and shipping

containerized

software.

Technical leadership

through mentoring and close collaboration with backend, data, product, and evaluation teams to convert feedback into lasting improvements.

Why this role is different

Grounding is non-negotiable : no fabricated or unsupported output, ever.

Faithfulness is enforced

with evaluation as a gate, not just a tuning target.

Separation of concerns

prioritizes ML surfaces and ensures generated text states what is supported by verified data or confirmed by a human.

Per-customer intelligence models

learn business behavior with strict anti-leakage discipline.

Customer-cloud deployments

including managed LLMs

in-VPC

so customer data does not leave their environment.

Nice to have

Finance or accounting domain experience.

Feature-attribution literacy, including experience with

SHAP .

Human-in-the-loop

systems, including review, confirmation, or correction workflows fed back into models.

Experience running managed LLMs inside customer clouds in a

VPC

(AWS Bedrock, Google Vertex AI, or Azure OpenAI).

Sparse and zero-inflated data experience.Multi-deployment enterprise software exposure, including multi-tenant SaaS, single-tenant SaaS, and on-premises across AWS, Azure, and GCP.

Technologies Python ,

scikit-learn ,

pandas ,

Polars ,

LightGBM ,

XGBoost ,

LangGraph ,

RAG ,

SQL ,

Snowflake ,

PostgreSQL ,

LangChain

#J-18808-Ljbffr