We are seeking a specialized AI & Data Intelligence Engineer to lead the accuracy, prompt

architecture, and statistical methodology capabilities of our enterprise platform for job

architecture and design, compensation management, and pay equity compliance.

Working alongside our data architect, development team, and domain experts, you will

directly manage the platform’s language model pipelines, structured schema enforcement,

version-controlled prompt templates, and Python-based statistical algorithms.

The primary responsibility of this role is to ensure that the product’s AI workflows and

statistical models operate reliably and deterministically, resist model and methodology drift,

and meet strict regulatory and auditability standards.

Key Responsibilities

LLM Engineering & Gateway Guardrails

● Author, maintain, improve, and version-control the platform’s prompt templates.

● Enforce zero-hallucination and structured-output policies using strict JSON Schema

validation and Pydantic models.

● Ensure low-confidence, invalid, or failed model outputs trigger automated retries or

human-in-the-loop review workflows.

● Manage the AI Gateway layer to capture and log token usage, model parameters,

confidence scores, and verbatim evidence citations.

Statistical Analytics Engine & Verification

● Co-own and enhance the pure-Python analytics engine responsible for the platform’s

primary statistical metrics and analytical calculations, working closely with the data

architect.

● Build and maintain statistical validation and testing harnesses that compare and

backtest production outputs against reference datasets, industry benchmarks, and

market data.

● Verify that statistical methodologies remain reproducible, version-controlled, and

consistent across releases.

Compliance, Governance & AI Integrity

● Enforce a strict architectural boundary in which AI is used for natural-language

parsing, interpretation, and drafting, while deterministic application code performs all

arithmetic, scoring, statistical calculations, and legal threshold checks.





● Support high-risk AI auditability and governance requirements by maintaining

version-controlled methodology definitions, prompt templates, model configurations,

and parameters in Git.

● Help ensure that AI-assisted workflows remain traceable, reproducible, and suitable

for regulated enterprise environments.

Explicit Technology Stack Breakdown

1. Technologies Owned & Managed by the AI Engineer

Hosted AI Inference

● Anthropic Claude models, including Claude Opus and Claude Sonnet, deployed

through Microsoft Foundry in an Azure EU region.

Prompting & Schema Validation

● JSON Schema

● Pydantic

● Jinja2 template engines

Statistical & Scientific Python

● Python 3.11+

● statsmodels

● scipy

● numpy

● pandas

● Statistical methods including OLS regression, Gelbach decomposition, robust

covariance estimation, log transformations, and related analytical techniques

Testing & Model Evaluations

● pytest

● Automated statistical comparison and regression-testing harnesses

● LLM evaluation frameworks such as DeepEval and Promptfoo

● Evaluation of model drift, citation accuracy, structured-output compliance, and

hallucination resistance

Version Control

● Git / GitHub

● Version management for prompts, methodologies, evaluation assets, and release

branches

2. Shared Application Touchpoints

Worker Execution

● Creating and managing task payloads for Celery workers and Redis queues

Data Layer

● Querying workspace and application schema tables in PostgreSQL through Python

and ORM-based data access

3. Technologies Managed by the Development Partner – Out of Scope for

This Role

Full-Stack Web Development

● Django 5





● Django REST Framework

● Vue 3

● Vite

Cloud & Web Infrastructure

● nginx

● Automated TLS

● Azure Virtual Networks

● Azure Blob Storage provisioning

● Docker container orchestration

● Deployment pipelines through GitHub Actions

Qualifications & Skills

Required

Education

Bachelor’s or Master’s degree in Data Science, Computer Science, Artificial Intelligence,

Machine Learning, Quantitative Economics, Statistics, or another related quantitative or

technical discipline.

LLM Prompt Engineering

Hands-on experience designing prompts, structured workflows, and orchestration pipelines

for frontier large language models, particularly Anthropic Claude deployed through Azure or

Microsoft Foundry.

Schema Validation & Guardrails

Advanced experience enforcing structured model outputs using JSON Schema, Pydantic,

validation logic, retry mechanisms, and human-review workflows.

Advanced Python & Statistics

Robust statistical programming skills using pandas, statsmodels, scipy, and related Python

libraries, including experience with OLS regression, transformations, robust standard errors,

covariance adjustments, and statistical validation.

Automated Testing & Evaluations

Demonstrated experience building automated evaluation and validation harnesses using

pytest, custom evaluation suites, or LLM evaluation frameworks to measure model drift,

citation accuracy, output consistency, schema compliance, and hallucination resistance.

Application Question(s):

- How many years of professional experience do you have in AI/ML, Generative AI, or LLM engineering?

- How many years of hands-on Python experience do you have, particularly in statistical modeling/data analysis?

- Have you worked with structured LLM outputs using JSON Schema and/or Pydantic?
- This is a 12-month contract position on hybrid for Mumbai location. Would you be comfortable with the contract duration?

Work Location: Hybrid remote in Mumbai, Maharashtra (Mumbai District)

📌 AI/ML Engineer (India)
🏢 begig
📍 India