JD – MLOps / LLMOps Engineer

Role Overview

We are seeking a mid-level MLOps / LLMOps Engineer to automate, operationalize, and monitor the lifecycle of traditional machine learning models and large language model applications. The role will work closely with Data Scientists, AI Engineers, Platform Engineers, Cloud Infrastructure teams, and Software Engineering teams to move models, prompts, RAG pipelines, and AI services from experimentation into secure, observable, and cost-efficient production environments without owning cloud resource provisioning.

Key Responsibilities

Model & LLM Application Operations: Package, configure, validate, and support ML models and LLM-powered applications for production release, working with the platform or cloud team for infrastructure deployment.

CI/CD and Release Automation: Design, build, and maintain release workflows for ML models, prompts, embeddings, RAG workflows, evaluation suites, and AI application configurations, in coordination with engineering and platform teams.

Monitoring, Observability & Evaluation: Implement monitoring for model drift, data drift, latency, throughput, quality, hallucination risk, retrieval quality, token usage, cost, and production reliability. Platform Integration Support: Integrate models, prompts, embeddings, vector search components, APIs, and monitoring hooks with existing cloud and platform services provisioned by the responsible infrastructure team.

LLMOps Enablement: Support prompt versioning, prompt testing, model gateway integration, multi-model routing, guardrails, safety checks, retrieval-augmented generation workflows, and rollback mechanisms for production GenAI systems.

Collaboration & Governance: Partner with Data Science, Engineering, Security, Compliance,



and Product teams to establish repeatable deployment standards, auditability, access controls, and responsible AI operating practices.

Qualifications & Skills

Experience: 2–4 years of hands-on experience in MLOps, DevOps, Platform Engineering, AI Engineering, or Software Engineering with exposure to production ML or GenAI systems.

Programming & Automation: Robust proficiency in Python, REST APIs, shell scripting, automation frameworks, testing practices, and production-grade coding standards.

ML, GenAI & LLM Tooling: Working knowledge of ML frameworks such as TensorFlow, PyTorch, and Scikit-Learn; experiment tracking tools such as MLflow or Weights & Biases; and LLM frameworks such as LangChain, LangGraph, LlamaIndex, or equivalent.

DevOps & Release Tools: Experience with Docker, Kubernetes concepts, GitHub Actions, Jenkins, GitLab CI, Azure DevOps, or similar CI/CD platforms; ability to collaborate with infrastructure teams on deployment readiness without owning cloud resource provisioning.

Cloud AI Services Awareness: Familiarity with Azure Machine Learning, Azure OpenAI, Azure AI Search, AWS SageMaker, Amazon Bedrock, GCP Vertex AI, or equivalent AI/ML services from an integration, operations, and monitoring perspective.

RAG & Vector Search: Understanding of embedding generation, vector databases, retrieval pipelines, chunking strategies, re-indexing, semantic search, and retrieval quality evaluation.

Observability & Governance: Experience or awareness of model monitoring, prompt/version governance, evaluation metrics, guardrails, security controls, audit trails, cost optimization, and responsible AI practices.

Preferred: Exposure to production GenAI platforms, model gateways, prompt registries, automated evaluation frameworks, human-in-the-loop feedback workflows, and agentic AI orchestration patterns.

📌 MLOps / LLMOps Engineer (India)
🏢 Hybrowlabs Technologies
📍 India