Ai Ml Engineer

Paperchase Accountancy; Ahmedabad District; Híbrido; INR 800.000 - 1.200.000; Jornada completa

Descripción de la vacante

Paperchase Accountancy seeks a proactive AI & Data Engineer to transform messy datasets into clean, scalable data pipelines. You will work with Power BI, DAX, PostgreSQL, SQL Server (SSMS), and Azure AI services in a hybrid Ahmedabad setup.

You’ll build ETL/ELT pipelines with Python, optimize AI/GenAI workflows, profile models for latency, and collaborate with teams to ensure reliable data delivery for analytics and ML applications.

Formación

• 2–4 years of hands-on experience in data engineering, analytics engineering, or applied machine learning.
• Solid working proficiency in PostgreSQL and SQL Server Management Studio (SSMS), including writing complex joins, indexing, and analyzing basic query execution plans.
• Practical experience writing and debugging DAX queries to fetch data out of Power BI models.
• Strong Python skills with libraries like Pandas, NumPy, and SQLAlchemy for data manipulation and cleaning.
• Understanding of ML/DL lifecycle and basic Generative AI concepts (LLM APIs, embeddings, vector search).

Responsabilidades

• Data Cleaning, Curation & Pipeline Development: Clean, normalize, and validate unstructured or messy data for analytics and AI models.
• Write and optimize DAX queries to programmatically extract and aggregate data directly from Power BI reports and datasets.
• Write performant SQL queries, stored procedures, and schema migrations across PostgreSQL and SQL Server (SSMS).
• Build, automate, and monitor reliable data pipelines (ETL/ELT) using Python and Azure cloud services. 2. AI, GenAI & Model Performance Optimization: Assist in developing and maintaining AI/GenAI workflows (RAG pipelines, prompt templates, vector search integrations with Azure OpenAI).
• Track and monitor model accuracy, latency, and throughput in staging and production.
• Version Control & CI/CD: Maintain clean, documented code and participate in peer code reviews using Git and GitHub.
• Work with automated build and deployment pipelines (GitHub Actions or Azure DevOps).

Conocimientos

Python

Pandas

NumPy

SQL

PostgreSQL

SSMS

DAX

Power BI

Azure

Git

CI/CD

ETL/ELT

GenAI concepts

ML/DL lifecycle

Model profiling

Herramientas

Azure Data Factory

Blob Storage/ ADLS

Azure OpenAI

ONNX runtime

GitHub Actions

Azure DevOps

LangChain

LlamaIndex

Tabular Editor

Descripción del empleo

Job Title: AI & Data Engineer Location:

Hybrid - Ahmedabad Office

Experience Level: JuniorMid Level (24 Years)

Department: Data Engineering & AI

About the Role We are looking for a proactive and detail-oriented AI & Data Engineer (24 years experience) to join our technical team. In this role, you will tackle real-world data challenges: taking messy, unorganized raw datasets and turning them into clean, fast, reliable data pipelines.

A central part of your day-to-day work will involve querying data from Power BI reports/semantic models using DAX, managing and optimizing relational stores in PostgreSQL and SQL Server (SSMS), and feeding curated data into our downstream AI, ML, and Generative AI systems. You will also assist in profiling and optimizing models to keep latency and compute costs low.

Key Responsibilities

1. 1. Data Cleaning, Curation & Pipeline Development
   • Clean, normalize, and validate unstructured or messy data to ensure highquality inputs for analytics and AI models.
   • Write and optimize DAX queries to programmatically extract and aggregate tabular data directly from Power BI reports and datasets.
   • Write performant SQL queries, stored procedures, and schema migrations across both PostgreSQL and SQL Server (SSMS).
   • Build, automate, and monitor reliable data pipelines (ETL/ELT) using Python and Azure cloud services. 2. AI, GenAI & Model Performance Optimization
2. 2. AI, GenAI & Model Performance Optimization
   • Assist in developing and maintaining AI/GenAI workflows (RAG pipelines, prompt templates, vector search integrations with Azure OpenAI).
   • Profile and optimize inference speed and resource usage for Machine Learning and GenAI systems: o Implement caching, prompt token reduction, and smart chunking strategies to minimize LLM latency and API bills. o Apply model optimization techniques (such as ONNX runtime conversion, quantization, batching, and GPU/CPU profiling).
   • Track and monitor model accuracy, latency, and throughput in staging and production.
3. 3. Statistical Analysis & Validation
   • Apply fundamental statistical concepts (summary statistics, distributions, hypothesis testing, data sampling, regression) to audit data distributions and evaluate model outputs. Identify anomalies, outliers, and data drift across pipeline stages.
4. 4. Version Control & CI/CD
   • Maintain clean, documented code and participate in peer code reviews using Git and GitHub.
   • Work with automated build and deployment pipelines (GitHub Actions or Azure DevOps).

Required Qualifications & Technical Skills

• Experience: 2 to 4 years of hands-on experience in data engineering, analytics engineering, or applied machine learning.
• Databases & Querying: o Solid working proficiency in both PostgreSQL and SQL Server Management Studio (SSMS), including writing complex joins, indexing, and analyzing basic query execution plans. o Practical experience writing and debugging DAX queries to fetch data out of Power BI models.
• Programming & Data Wrangling: o Strong Python skills with libraries like Pandas, NumPy, and SQLAlchemy for data manipulation and cleaning.
• AI & Optimization Foundations: o Understanding of ML/DL lifecycle and basic Generative AI concepts (LLM APIs, embeddings, vector search). o Hands-on exposure to latency optimization (caching, batch inference, token usage control, ONNX/runtime speedups).
• Mathematics & Statistics: Solid understanding of probability, distributions, data validation methods, and evaluation metrics (precision, recall, MSE, perplexity).
• Cloud & Collaboration Tools: o Exposure to Microsoft Azure services (Azure Data Factory, Blob Storage / ADLS, Azure OpenAI). o Daily proficiency with Git and GitHub (branching, PR workflows, resolving merge conflicts).

Nice-to-Have Skills

• Familiarity with tools like DAX Studio or Tabular Editor.
• Experience with LangChain, LlamaIndex, or Azure AI Search.
• Any foundational Microsoft Azure certification (e.g., DP-900, DP-203, or AI900).