SC
Scribe Emr Talent Lead
β Verified Recruiter
Direct Job Poster Contact
π’ Scribe Emr β’ Direct Recruiter Email:
vacancies@scribeemr.in
Required Skills & Tech Stack
Technical management
Voice
Artificial Intelligence
Pyton
Technical
Management
π Free Study Materials (4)
π
Aptitude Practice Questions & Mock Tests
Open Resource β
Curated logical, quantitative, and verbal reasoning problems for the initial online screening round.
π―
Company-Specific Interview Preparation Corner
Open Resource β
Detailed interview experiences, exam formats, and previous test questions for Scribe Emr and tech roles.
π
Technical Placement Cheat Sheets & Question Bank
Open Resource β
High-yield coding cheat sheets, core CS fundamentals (OOP, DBMS, OS, Networks), and rapid revision guides.
π»
Algorithm, DSA & Live Code Debugger Practice
Open Resource β
Hands-on problem sets to improve coding speed and step-by-step memory debugging.
Eligibility Criteria
Experience: 7-12 Yrs. Open to matching qualifications.
Selection & Interview Process
1
Online Assessment / Technical Interview Rounds / HR Review
Job Description & Key Responsibilities
ScribeEMR India Pvt. Ltd. is a healthcare technology and services company supporting U.S. healthcare organizations with a comprehensive range of solutions, including medical scribing, medical coding, revenue cycle management (RCM), virtual medical office services, and healthcare-related software development. 1. Official Name of the Designation: Technical Manager β US healthcare AI Voice Agents 2. Work Location: Hebbal Ring Road, Bengaluru 3. Work Timings: Full-time 4. Educational Qualifications/Skillsets Required: Bachelorβs or Masterβs degree in Computer Science, Software Engineering, or a related field. 8β12 years of progressive software engineering experience with at least 4β6 years in a technical lead or manager capacity. Hands-on experience building and deploying voice/conversational AI systems, speech to-text (STT), text-to-speech (TTS), and dialogue management platforms in production. Strong understanding of LLM-powered conversational agents, prompt engineering, function calling, and agentic workflows (LangChain, LlamaIndex, Semantic Kernel). Familiarity with U.S. healthcare workflows β patient scheduling, insurance verification, prior authorization, prescription refills, appointment reminders, and revenue cycle processes. Page 1 of 3 Leader in Virtual Medical Office Solutions Experience with telephony and voice infrastructure β SIP, VoIP, WebRTC, Twilio, Amazon Connect, or Genesys; real-time audio streaming and low-latency voice pipelines. Proficiency in Python and modern ML frameworks (PyTorch, TensorFlow); working knowledge of Node.js, ReactJS, or .NET for full-stack integration . Experience with AWS, Azure, or GCP; containerization (Docker, Kubernetes); CI/CD tooling; and infrastructure-as-code (Terraform, CloudFormation). Proficiency in relational (PostgreSQL, SQL Server) and NoSQL (MongoDB, Redis, Elasticsearch) databases; experience with vector databases (Pinecone, Weaviate) is a plus. Proactive, self-starter mindset; excellent verbal and written English; strong client and stakeholder communication skills. How to Apply: Apply through our website https://www.scribeemr.in/ NOTES: Please visit the following website to know more about us https://www.scribeemr.in/ If you still have any questions, please write to vacancies@scribeemr.in
Preparation Hub
Scribe Emr β AI & Machine Learning Interview Guide
Previously asked questions, exam syllabus, coding benchmarks & round strategy.
π Previously Asked Questions & Preparation Links
APTITUDE & LOGIC
π
Aptitude Practice Questions & Online Mock Tests
Curated logical, quantitative, and verbal reasoning problems to sharpen reasoning skills required for the initial screening test.
Open Resource β
COMPANY GUIDE
π―
Scribe Emr Interview Preparation Corner
Comprehensive guide covering typical interview formats, common questions, and preparation tips for Scribe Emr and off-campus tech roles.
Open Resource β
MOCK TESTS & PAPERS
π
Comprehensive Interview Prep Resources & Syllabus
Collection of previous year questions, company-specific test patterns, and interview experiences for technical and HR rounds.
Open Resource β
CODING PRACTICE
π»
Algorithm and Data Structure Problem Set
Practice problems to improve coding proficiency and algorithm problem-solving speed for technical rounds.
Open Resource β
π» Previously Asked Coding Challenges
Easy / Medium
β‘ Live Debugger
Two Sum & Two Pointers Array Optimization
Asked in 85% tech rounds
Medium
β‘ Live Debugger
Reverse Linked List & Detect Cycle (Floyd's Algorithm)
Frequently asked in core rounds
Medium
β‘ Live Debugger
SQL Nth Highest Salary with DENSE_RANK() & Subqueries
Top standard database question
Easy / Medium
β‘ Live Debugger
Valid Parentheses & Stack Implementation
Standard online assessment problem
πΊοΈ Round-Wise Strategy
Round 1: Online Assessment (OA)
Aptitude, Quantitative Logic & 2 Coding Problems
Focus on accuracy and speed. Practice arrays, strings, and standard arithmetic puzzles.
Round 2: Technical Interview 1
Core Tech Stack (Technical management, Voice, Artificial Intelligence, Pyton, Technical, Management) & Live Code Tracing
Explain your thought process aloud. Analyze time & space complexities before coding.
Round 3: System Design & Problem Solving
Database Schemas, APIs & Architecture Basics
Clarify edge cases, diagram schemas cleanly, and discuss scalability trade-offs.
Round 4: HR & Cultural Fit Discussion
Scribe Emr Core Values, Learning Agility & Offer Terms
Demonstrate passion, strong communication, and readiness for full-time collaboration.
β Core Technical Questions & Answers
What is the bias-variance tradeoff and how do you prevent overfitting? Answer βΌ
Model Answer: High bias leads to underfitting (oversimplified model), high variance leads to overfitting (captures noise). Mitigate using L1/L2 Regularization, Dropout, Cross-Validation, and data augmentation.
Explain the difference between Precision, Recall, and F1-Score. Answer βΌ
Model Answer: Precision = TP / (TP + FP) (correctness of positive predictions). Recall = TP / (TP + FN) (coverage of actual positives). F1-Score is the harmonic mean of Precision and Recall.
How does Gradient Descent work and what is the role of Learning Rate? Answer βΌ
Model Answer: It optimizes loss functions by iteratively moving weights in the direction of negative gradient. A large learning rate may overshoot the minimum; a small rate causes slow convergence.
What is the difference between Supervised, Unsupervised, and Self-Supervised learning? Answer βΌ
Model Answer: Supervised uses labeled data (X -> y). Unsupervised finds hidden patterns in unlabeled data (clustering/PCA). Self-supervised generates labels from input data (e.g. masked language modeling in BERT/Transformers).
π€ Behavioral & HR Questions (STAR Method)
Why do you want to join Scribe Emr as a Technical Manager,AI VoiceAgent?
Preparation Tip: Highlight Scribe Emr's market reputation, recent tech innovations, and how your skills in Technical management directly solve their team's objectives.
Describe a challenging bug or academic project roadblock and how you resolved it.
Preparation Tip: Use the STAR method: Situation (project context), Task (what needed solving), Action (specific tools/logic applied), Result (quantifiable positive outcome).
How do you handle strict deadlines or sudden scope changes?
Preparation Tip: Explain your prioritization strategy, proactive communication with mentors/peers, and agile mindset.
