AI Engineer - Backend

Meraki Labs; Bengaluru; Hybrid; INR 4,000,000 - 7,000,000; Full time

Job summary

Meraki Labs in Bengaluru seeks an AI engineer to own the loop turning real-world patient interactions into smarter AI agents, from voice-enabled activation to end-to-end care workflows.

You will design evaluation pipelines, capture the right signals from product usage, and create reinforcement learning loops that drive continuous quality improvement across every agent we ship, with production-grade systems and scalable architectures.

Qualifications

• You've built AI agents or LLM-powered systems in production.
• Experience with evaluation pipelines and metrics from real usage.
• Familiarity with RLHF or feedback-driven improvement for LLMs.
• Proven ability to deploy and monitor AI systems with real users.
• Experience with healthcare AI workflows is a plus.

Responsibilities

• Launch and scale high-quality AI agents.
• Build and deploy voice AI for patient activation and outbound calls.
• Design agents to orchestrate end-to-end care workflows.
• Build copilots to assist advocates in real time.
• Develop evaluation and RL pipelines for continuous improvement.
• Set up metrics and data capture to improve agent quality.
• Document systems and prepare for scale.
• Collaborate with founders to shape the technical roadmap.

Skills

AI agents

RLHF

Evaluation pipelines

Production systems

Voice/orchestration

Job description

We're looking for an AI engineer to own the loop that turns real-world patient interactions into better AI agents, from eval infrastructure to production-grade systems. You'll own the systems that make our agents smarter with every patient interaction, from the voice AI that activates patients and handles calls to insurance, vendors, and providers, to the autonomous workflows that orchestrate end-to-end care delivery. You'll work directly with the founders to build production systems that learn and improve from real-world usage. You'll design the eval pipelines, capture the right signals from product usage, and create the reinforcement learning loops that drive continuous quality improvement across every agent we ship. If you've built AI systems that get better with usage and want to apply that experience to healthcare, where every improvement directly impacts patient outcomes, we'd love to talk.

Responsibilities:

• Launch and scale high-quality AI agents.
• Build and deploy voice AI for automating patient activation and outbound calls to insurance, vendors, and providers.
• Design individual agents that orchestrate end-to-end workflows such as DME delivery, prior authorisations, and care coordination.
• Build copilots that assist and nudge advocates in real time.
• Build the eval and reinforcement learning pipeline.
• Set up the evaluation infrastructure that measures agent quality across every interaction.
• Capture the right data from product usage, build the feedback loop, and implement RL pipelines so that agent performance improves continuously with real-world usage.
• Create the usage-driven product improvement flywheel.
• Scale AI Nurse by composing multiple agents.
• Bring together voice, workflow, and copilot agents into a unified AI Nurse experience.
• Ensure that as we scale, each agent reinforces the others through shared learning and a consistent improvement loop.
• Ship fast and iterate with real users.
• Deploy to production, monitor how agents perform with actual patients, and improve based on real conversations.
• Own the tight loop from usage data to system-level improvements.
• Shape the technical roadmap.
• Work with the founders to decide what to build next.
• Bring deep knowledge of what's possible with current AI capabilities and help us make smart bets on where the technology is heading.
• Lay the foundation for scale.
• Make architectural decisions that will hold up as we grow from hundreds of patients to hundreds of thousands.
• Document systems, establish best practices, and build with the next engineer in mind.

Requirements:

• You've built AI agents or LLM-powered systems from scratch and shipped them to production, not just demos, but systems handling real interactions with real users at scale.
• You have hands‑on experience building evaluation pipelines for AI systems, designing metrics, capturing signals from production usage, and using that data to systematically improve agent quality.
• You have experience with reinforcement learning from human feedback (RLHF), reward modelling, or other feedback‑driven improvement loops for LLM-based systems.
• You've monitored and debugged AI systems in production, you know what it takes to keep agents reliable when they're interacting with real people. You're comfortable with ambiguity.
• The playbook doesn't exist yet, and you're excited to build it.

Nice to Have:

• Experience in healthcare, health tech, or regulated industries (HIPAA, PHI handling).
• Familiarity with Medicare, insurance workflows, or clinical operations.
• Experience with voice AI systems, speech‑to‑text, text‑to‑speech, and real‑time voice orchestration.
• Background in RAG systems, vector databases, and knowledge retrieval pipelines.
• Contributions to open‑source AI projects or a portfolio of side projects that show your range.

Good to Have:

• Beyond technical skills, we're looking for someone who embodies the attributes that make great engineers at an early‑stage company.
• Proactive. You move quickly and take a forceful stand without being abrasive.
• You act without being told what to do and bring new ideas to the company.
• Analytically sharp. You structure and process qualitative or quantitative data and draw penetrating insights.
• You learn quickly and absorb new information with ease.
• High standards with attention to detail. You expect nothing short of the best from yourself and your team.
• You don't let important details slip through the cracks or derail a project.
• Passionate and open. You exhibit enthusiasm and a can‑do attitude toward your work.
• You solicit feedback often and react calmly to criticism or negative feedback.