11+ Excel Modeling Jobs in Delhi, NCR and Gurgaon | Excel Modeling Job openings in Delhi, NCR and Gurgaon

About MyOperator

MyOperator is a Business AI Operator, a category-leader that unifies WhatsApp, Calls, and AI-powered chat & voice bots into one intelligent business communication platform. Unlike fragmented communication tools, MyOperator combines automation, intelligence, and workflow integration to help businesses run WhatsApp campaigns, manage calls, deploy AI chatbots, and track performance — all from a single, no-code platform. Trusted by 12,000+ brands including Amazon, Domino's, Apollo, and Razorpay, MyOperator enables faster responses, higher resolution rates, and scalable customer engagement — without fragmented tools or increased headcount

Role Summary

We’re hiring a Front Deployed Engineer (FDE)—a customer-facing, field-deployed engineer who owns the end-to-end delivery of AI bots/agents.

This role is “frontline”: you’ll work directly with customers (often onsite), translate business reality into bot workflows, do prompt engineering + knowledge grounding, ship deployments, and iterate until it works reliably in production.

Think: solutions engineer + implementation engineer + prompt engineer, with a strong bias for execution.

Responsibilities-

Requirement Discovery & Stakeholder Interaction

• Join customer calls alongside Sales and Revenue teams.
• Ask targeted questions to understand business objectives, user journeys, automation expectations, and edge cases.
• Identify data sources (CRM, APIs, Excel, SharePoint, etc.) required for the solution.
• Act as the AI subject-matter expert during client discussions.

Use Case & Solution Documentation

• Convert discussions into clear, structured use case documents, including:
• Problem statement & goals.
• Current vs. proposed conversational flows.
• Chatbot conversation logic, integrations, and dependencies.
• Assumptions, limitations, and success criteria.

Customer Delivery Ownership

• Own deployment of AI bots for customer use-cases (lead qualification, support, booking, etc.). Run workshops to capture processes, FAQs, edge cases, and success metrics. Drive the go-live process: requirements through monitoring and improvement.

Prompt Engineering & Conversation Design

• Craft prompts, tool instructions, guardrails, fallbacks, and escalation policies for stable behavior. Build structured conversational flows: intents, entities, routing, handoff, and compliant responses. Create reusable prompt patterns and "prompt packs."

Testing, Debugging & Iteration

• Analyze logs to find failure modes (misclassification, hallucination, poor handling). Create test sets ("golden conversations"), run regressions, and measure improvements. Coordinate with Product/Engineering for platform needs.

Integrations & Technical Coordination

• Integrate bots with APIs/webhooks (CRM, ticketing, internal tools) to complete workflows. Troubleshoot production issues and coordinate fixes/root-cause analysis.

What Success Looks Like

• Customer bots go live quickly and show high containment + high task completion with low escalation.
• You can diagnose failures from transcripts/logs and fix them with prompt/workflow/knowledge changes.
• Customers trust you as the “AI delivery owner”—clear communication, realistic timelines, crisp execution.

Requirements (Must Have)

• 2–5 years in customer-facing delivery roles: implementation, solutions engineering, customer success engineering, or similar.
• Hands-on comfort with LLMs and prompt engineering (structured outputs, guardrails, tool use, iteration).
• Strong communication: workshops, requirement capture, crisp documentation, stakeholder management.
• Technical fluency: APIs/webhooks concepts, JSON, debugging logs, basic integration troubleshooting.
• Willingness to be front deployed (customer calls/visits as needed).

Good to Have (Nice to Have)

• Experience with chatbots/voicebots, IVR, WhatsApp automation, conversational AI platforms with at least a couple of projects. 
• Understanding of metrics like containment, resolution rate, response latency, CSAT drivers.
• Prior SaaS onboarding/delivery experience in mid-market or enterprises.

Working Style & Traits We Value

• High agency: you don’t wait for perfect specs—you create clarity and ship.
• Customer empathy + engineering discipline.
• Strong bias for iteration: deploy → learn → improve.
• Calm under ambiguity (real customer environments are chaotic by default).

Cold Calling, Business Development and Lead Generation via Phone, Email and Web.

Direct Selling and Target Based (Field Sales/Out Side Sales).

Prepare and follow up on sales quotations made to clients.

Will be responsible for client retention, per client revenue optimization & renewals.

Handle client queries/escalations.

Should have effective sales & negotiation ability.

Interacting with vendors for product upload in their respective Domain.

Manage and create product listing and another day to day activities on portal

Improve Customer perception and product-wise market share, a good understanding of technical and market feasibility of the product.

Must have a Laptop & Bike

A Financial Research Analyst is responsible for gathering, analyzing, and interpreting financial data to support investment decisions, business strategies, or financial forecasting. Their primary role is to provide insights and recommendations based on their research to help guide the organization or clients in making informed financial decisions.

Key Responsibilities:

Data Collection & Analysis:

• Gather financial data from various sources (e.g., financial statements, market reports, industry trends, etc.).
• Analyze financial information, including profit and loss statements, balance sheets, and cash flow reports.

Market and Industry Research:

• Research industry trends, economic conditions, and market conditions to identify investment opportunities or risks.
• Track competitor performance and benchmarking.

Financial Modeling and Forecasting:

• Build and maintain financial models to project future performance, such as forecasting revenue and expenses.
• Use quantitative tools to evaluate investment options and potential financial outcomes.

Report Creation:

• Prepare detailed reports summarizing findings, analyses, and recommendations for internal stakeholders or clients.
• Present research findings to management, clients, or investors in a clear and concise manner.

Investment Recommendations:

• Provide recommendations for investment opportunities, such as stocks, bonds, or alternative investments.
• Assist with the evaluation of portfolios and asset management strategies.

Risk Assessment:

• Assess financial risks related to investments, projects, or business decisions.
• Recommend risk management strategies based on data analysis.

Collaboration & Communication:

• Work closely with other teams, such as investment managers, accountants, or business strategists, to align research with broader business objectives.
• Communicate findings and insights clearly to both financial and non-financial stakeholders.

Role - MLops Engineer

Location - Pune, Gurgaon, Noida, Bhopal, Bangalore 

Mode - Hybrid

Role Overview

We are looking for an experienced MLOps Engineer to join our growing AI/ML team. You will be responsible for automating, monitoring, and managing machine learning workflows and infrastructure in production environments. This role is key to ensuring our AI solutions are scalable, reliable, and continuously improving.

Key Responsibilities

• Design, build, and manage end-to-end ML pipelines, including model training, validation, deployment, and monitoring.
• Collaborate with data scientists, software engineers, and DevOps teams to integrate ML models into production systems.
• Develop and manage scalable infrastructure using AWS, particularly AWS Sagemaker.
• Automate ML workflows using CI/CD best practices and tools.
• Ensure model reproducibility, governance, and performance tracking.
• Monitor deployed models for data drift, model decay, and performance metrics.
• Implement robust versioning and model registry systems.
• Apply security, performance, and compliance best practices across ML systems.
• Contribute to documentation, knowledge sharing, and continuous improvement of our MLOps capabilities.

Required Skills & Qualifications

• 4+ years of experience in Software Engineering or MLOps, preferably in a production environment.
• Proven experience with AWS services, especially AWS Sagemaker for model development and deployment.
• Working knowledge of AWS DataZone (preferred).
• Strong programming skills in Python, with exposure to R, Scala, or Apache Spark.
• Experience with ML model lifecycle management, version control, containerization (Docker), and orchestration tools (e.g., Kubernetes).
• Familiarity with MLflow, Airflow, or similar pipeline/orchestration tools.
• Experience integrating ML systems into CI/CD workflows using tools like Jenkins, GitHub Actions, or AWS CodePipeline.
• Solid understanding of DevOps and cloud-native infrastructure practices.
• Excellent problem-solving skills and the ability to work collaboratively across teams.

JD -

Given our business model, where our customers pay us over time, Customer experience is absolutely vital to our long-term profitability. We will not be successful unless our customers are receiving massive value from our service. As such, we need a leader to own driving success for our customers. This role includes responsibilities for Customer experience, retention and Customer Success (e.g., onboarding, support, services, adoption, advocacy, retention, etc.)

• Drive Customer Experience Outcomes
• Define and Optimize Customer Lifecycle
• Manage Customer Experience Activities
• Measure the Effectiveness of Customer Experience processes
• Build a World-class Team
• Inspire Customer Success Across Company

Requirements -

• 1-4 years experience in leading customer-facing organizations
• Ability to manage influence through persuasion, negotiation, and consensus building
• Ideally combined background in E-commerce or Saas industries
• Strong empathy for customers AND passion for revenue and growth
• Deep understanding of value drivers in recurring revenue business models
• Analytical and process-oriented mindset
• Demonstrated desire for continuous learning and improvement
• Enthusiastic and creative personality with the ability to inspire others
• Excellent communication and presentation skills