AI Engineer

Recro; Bengaluru; On-site; INR 1,200,000 - 1,800,000; Full time

Job summary

Recro is seeking an AI Engineer to own production-grade AI systems end-to-end — from LLM applications and agentic workflows to model serving, evaluation, and monitoring. The role emphasizes hands-on work in Agentic AI, LLM orchestration, and self-hosted models, with responsibilities spanning from development to observability.

The ideal candidate will work with Docker, Kubernetes, Python, PyTorch, and vector databases to build scalable AI solutions, including RAG pipelines and memory systems.

Qualifications

• Experience in Agentic AI, LLM orchestration, self-hosted models, and production ML engineering.
• Ability to build AI agents, LLM workflows, and orchestration systems.
• Hands-on with RAG pipelines, memory systems, and retrieval workflows.

Responsibilities

• Build AI agents, LLM workflows, and orchestration systems.
• Develop RAG pipelines, memory systems, and retrieval workflows.
• Work with LLM gateways and agent frameworks.
• Improve reliability and performance of LLM applications.
• Deploy and optimize self-hosted AI models.
• Experience with Docker, Kubernetes, and cloud infrastructure.
• Build production AI systems using Python.
• Work with PyTorch, Hugging Face, Transformers, and vector databases.
• Build evaluation frameworks and benchmark datasets.
• Track AI quality, latency, cost, and reliability metrics.
• Set up monitoring, dashboards, and alerts for production AI systems.
• Experience with STT/ASR is a plus.

Skills

Agentic AI

LLM orchestration

LLM Gateway

RAG

Vector databases

vLLM / Triton

Python

PyTorch

MLOps

Tools

Docker

Kubernetes

Cloud infrastructure

vLLM / Triton

Hugging Face

Transformers

Job description

AI Engineer – Agentic AI & Production ML

Experience

3–7 Years

Role Overview

We are looking for an AI Engineer to build and own production-grade AI systems end-to-end — from LLM applications and agentic workflows to model serving, evaluation, and monitoring.

The role requires strong hands-on experience in Agentic AI, LLM orchestration, self-hosted models, and production ML engineering.

Responsibilities

Agentic AI & LLM Systems (Must Have)

• Build AI agents, LLM workflows, and orchestration systems.
• Develop RAG pipelines, memory systems, and retrieval workflows.
• Work with LLM gateways and agent frameworks.
• Improve reliability and performance of LLM applications.

Model Serving & Infrastructure (Must Have)

• Deploy and optimize self-hosted AI models.
• Experience with vLLM / Triton or similar serving frameworks.
• Optimize latency, scalability, and inference cost.
• Work with Docker, Kubernetes, and cloud infrastructure.
• Build production AI systems using Python.
• Work with PyTorch, Hugging Face, Transformers, and vector databases.

Evaluation & Observability

• Build evaluation frameworks and benchmark datasets.
• Track AI quality, latency, cost, and reliability metrics.
• Set up monitoring, dashboards, and alerts for production AI systems.

Speech AI (Good to Have)

• Experience with STT, ASR, speaker diarization, or voice AI systems.

Required Skills

Must Have

• Agentic AI
• LLM / Generative AILLM Orchestration
• LLM Gateway
• RAG
• Vector Databases
• vLLM / Triton
• Python
• PyTorch
• MLOps

Good to Have

• RLHF
• AI Evaluation
• Open-source AI contributions

Ideal Candidate Profile

• Has shipped AI products into production.
• Strong understanding of LLM systems and AI architecture.
• Can own complete AI systems, not just model tuning.
• Demonstrates strong engineering fundamentals and problem-solving ability.

Hiring benchmark:

Candidates should demonstrate strong Agentic AI depth, production ML experience, and ownership of scalable AI systems.