AI/ML Engineer – Noida

Opportunity Overview

Core Lifecycle & Technical Architecture

The AI/ML Engineer oversees the entire model lifecycle—from raw data processing and model construction to scalable API deployment, live monitoring, and client-facing solution architecture:

┌───────────────────────────────────────────┐
│ 1. Data Processing, NLP & OCR Extraction │ ➔ OpenCV, Tesseract, SpaCy, NLTK
└─────────────────────┬─────────────────────┘
                      ▼
┌───────────────────────────────────────────┐
│ 2. ML Model Building & Optimization      │ ➔ TensorFlow, PyTorch, Scikit-Learn
└─────────────────────┬─────────────────────┘
                      ▼
┌───────────────────────────────────────────┐
│ 3. API & Queue Development                │ ➔ FastAPI / Flask + Celery / Redis
└─────────────────────┬─────────────────────┘
                      ▼
┌───────────────────────────────────────────┐
│ 4. MLOps Deployment & Cloud Pipeline      │ ➔ AWS / GCP Cloud Pipelines
└─────────────────────┬─────────────────────┘
                      ▼
┌───────────────────────────────────────────┐
│ 5. Live Model Monitoring & Client Demo    │ ➔ Production Monitoring & Pre-Sales Support
└───────────────────────────────────────────┘

Key Responsibilities

• Model Design & Optimization: Build, train, fine-tune, and optimize predictive and classification machine learning models for production performance and scalability.

• NLP & OCR Implementation: Develop text extraction, natural language processing, and optical character recognition pipelines using tools like Tesseract, OpenCV, SpaCy, and NLTK.

• Backend API Development: Wrap ML models inside high-throughput REST APIs (FastAPI, Flask, or Django) and handle asynchronous background workloads using queuing systems (RabbitMQ, Redis, Celery).

• MLOps & Cloud Deployment: Deploy, host, and monitor live production models on cloud infrastructure (AWS or GCP) using standardized MLOps practices.

• Pre-Sales & Client Demonstration: Collaborate with sales and business development teams to understand client objectives, design technical proposals, and demonstrate live AI solutions to stakeholders.

Technical & Qualification Requirements

Essential Qualifications & Experience

• Education: Bachelor’s or Master’s degree in Computer Science, Data Science, Machine Learning, or a related quantitative engineering field.

• Experience Range: 3 to 6 years of industry experience, with a minimum of 3–4 years of hands-on experience building and deploying machine learning models, NLP pipelines, and OCR engines in production environments.

Technical Skill Matrix

Application & Assessment Preparation

1. Portfolio Focus: Highlight end-to-end production pipelines—especially those combining document processing (OCR), text classification/NLP, and asynchronous API architectures (FastAPI + Celery/Redis).

2. Technical Interview Preparation:

   • NLP & OCR: Practice preprocessing techniques for noisy text, image thresholding/contour detection in OpenCV, and named entity recognition (NER) in SpaCy.

   • System Design: Prepare to design a scalable asynchronous ML inferencing backend handling high-concurrency requests.

   • Model Fine-Tuning & Evaluation: Be ready to explain trade-offs between precision, recall, F1-score, inference latency, and model compression techniques.