MEHRA/INNOVATIONS
The Crew

Every person we place clears the fluency bar.

The bench is the proof.

Not the deck.

Not the case study.

The people we put in front of you, on week one.

Elite AI-fluent engineering crew standing amid holographic evaluation dashboards and knowledge-graph nodes

What AI-fluent means at MI.

Every person on the bench can do all four of these before we place them:

  • —Run production code with an AI coding assistant in their daily flow.
  • —Build and run at least one eval-driven workflow.
  • —Explain the trade-offs between RAG, fine-tuning, and prompt engineering for a real use case.
  • —Run at least one feature where an LLM is in the production path.

If a candidate can't do those four in our screen, we don't place them. This is the line that separates us from generalist staffing firms now adding AI to their LinkedIn.

AI-fluent engineer pair-programming with an AI assistant against evaluation dashboards

Roles on the crew.

Cross-disciplinary AI-fluent team collaborating around a holographic knowledge graph
01 / 04

Data

  • —Data engineers — batch and streaming pipelines, lakehouse, event-driven
  • —Analytics engineers — dbt, semantic layer, metrics
  • —ML engineers — training, deployment, monitoring, drift
  • —Applied scientists and data scientists
  • —Data architects — graph, lakehouse, real-time
  • —ML / AI Ops engineers — model registry, eval infra, observability
  • —Entity resolution and knowledge graph engineers — substrate engineers behind BorrowerGraph, SMB Entity Hub, EvidenceGraph, DecisionFabric
  • —Data governance and data product managers
02 / 04

Engineering

  • —AI / LLM application engineers — RAG, agents, evals in the production path
  • —Backend, frontend, and full-stack engineers
  • —Platform / DevOps / SRE engineers
  • —Cloud engineers — AWS, Azure, GCP
  • —Mobile engineers — iOS, Android
  • —Solutions and enterprise architects
  • —Engineering and tech leads
  • —Security engineers — AppSec and AI security (prompt injection, model abuse, data exfiltration)
03 / 04

Quality

  • —Quality engineers and SDETs
  • —Test automation engineers
  • —Performance and load test engineers
  • —Eval engineers — LLM evals, agent evals, RAG evals, regression sweeps
  • —Test data engineers — synthetic data, edge-case generation
  • —Quality leads
04 / 04

Product, design, research

  • —AI-fluent product managers
  • —AI-fluent product designers
  • —Prompt and eval engineers (cross-discipline)
  • —Applied AI researchers
Geography

US-primary.

Offshore from India.

Global delivery corridors between the United States and India

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