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.

• 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.

• 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 Lang Graph 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.

• 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.

• 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.

Python
, scikit-learn
, pandas
, Polars
, LightGBM
, XGBoost
, Lang Graph
, RAG
, SQL
, Snowflake
, PostgreSQL
, Lang Chain