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Intern - AI Product Operations & Solutions (Delivery Team)

Apna

Apna

Software Engineering, Product, Operations, Data Science
Bengaluru, Karnataka, India
Posted on Mar 25, 2026

Intern – AI Product Operations & Solutions (Delivery Team)

Location: Bangalore (Domlur)
Mode: Work from Office
Duration: 90 days
Full-Time Opportunity: Interns who demonstrate strong performance during the internship may be considered for full-time opportunities at Blue Machines, subject to business requirements.

About Blue Machines

Blue Machines is building one of the fastest enterprise Voice AI platforms, deploying agentic AI systems for leading enterprises across airlines, banking, mutual funds, and retail. Our systems operate at production scale, handling millions of real-world conversations across customer support, sales, collections, and operations.

We work on real deployments, real workflows, and real business outcomes.

Role Overview

As an Intern in the Delivery Team, you will work on live enterprise AI deployments, with a strong focus on testing, evaluating, and improving AI voice agents.This role sits at the intersection of:

AI Systems × Prompt Design × Evaluation × Workflow Mapping × Analytics

You will work closely with the delivery and solutions team to understand client workflows, test conversational agents across multiple scenarios, analyze agent performance, and support continuous improvement before and after deployment.

Key Responsibilities

1. Agent Testing & Analysis

  • Test AI voice agents across scenarios, edge cases, and workflow variations
  • Identify issues in conversation flows, including logic breaks, missed intents, compliance gaps, and hallucinations
  • Analyze call transcripts, logs, and agent behaviour to generate performance insights
  • Support evaluation frameworks across metrics such as accuracy, containment, latency, and adherence to expected flows

2. Prompt Iteration

  • Assist in designing and refining prompts for conversational agents
  • Run iterative experiments to improve response quality, task completion, and user experience
  • Document observations and recommend improvements based on testing outcomes

3. Workflow Understanding

  • Map customer journeys and conversation workflows for different use cases
  • Understand how AI agents interact with backend systems such as CRM, APIs, and telephony platforms
  • Support workflow configuration and logic validation during deployment cycles

4. Monitoring & Metrics

  • Track key metrics such as containment rate, drop-offs, response accuracy, and failure patterns
  • Review agent performance dashboards and highlight areas for optimization
  • Help convert raw observations into actionable recommendations

5. Deployment Support

  • Support QA, testing, and documentation during deployment cycles
  • Assist with change logs, issue tracking, and post-deployment optimization
  • Work with internal teams to ensure agents are production-ready

What You’ll Learn

  • Prompt engineering for production AI systems
  • AI evaluation and performance benchmarking
  • Enterprise AI deployment lifecycle
  • Workflow design for conversational systems
  • Real-world conversational data analysis

Who You Are

  • Final-year student or recent graduate in Engineering, Computer Science, Operations, or a related field
  • Strong analytical and problem-solving skills
  • High attention to detail, especially in testing and evaluation
  • Comfortable working in ambiguous, fast-moving environments
  • Clear written communication and structured thinking
  • Curious about AI systems and how they perform in production

Good to Have

  • Exposure to LLMs, prompt engineering, or conversational AI tools
  • Basic understanding of APIs, workflow systems, or integrations
  • Experience with data analysis, QA, or structured testing

Why This Role

  • Work on production AI systems, not mock projects
  • Gain hands-on exposure to enterprise-scale AI deployments
  • Build strong foundations in AI evaluation, systems thinking, and workflow design
  • Opportunity to convert into a full-time role based on performance

Success Traits

  • Structured thinking
  • Strong ownership mindset
  • Fast learning and iteration
  • Ability to test rigorously and communicate clearly