Job Description & How to Apply Below
AI Engineer – Agentic AI & Production ML
Experience
3–5 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.
AI Engineering
Build production AI systems using Python.
Work with PyTorch, Hugging Face, Transformers, and vector databases.
Own AI lifecycle: development → deployment → monitoring → improvement.
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 AI
LLM Orchestration
LLM Gateway
RAG
Vector Databases
Self-hosted Models
vLLM / Triton
Python
Py Torch
Hugging Face
Kubernetes
MLOps
Good to Have
Speech AI / STT
RLHF
Reinforcement Learning
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.
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