AI/AIOps Engineer – Production ML & Observability

Cognizant; Región Centro; On-site; MXN 350,000 - 600,000; Full time

Job summary

Cognizant is hiring an AI/AIOps Engineer to design, build, and productionize AI/ML and LLM systems. You will work on GenAI/LLM engineering including RAG, embeddings, and tool-driven automation with guardrails and human approvals.

The role requires hands-on production Python code, ML libraries, and strong troubleshooting across Linux, Java, and Python environments, with observability and RCA/AIOps tooling experience. This is a growth-focused position in a dynamic team.

Qualifications

• Production AI/ML experience in real user production systems.
• Experience with GenAI/LLM engineering including RAG and embeddings.
• Experience with agentic AI agents or multi-step automations and tooling guardrails.
• Defined evaluation sets and metrics to improve systems based on results.
• AIOps experience with time-series anomaly detection and alert-noise reduction.
• Proficient Python production code with tests and common ML libraries.
• Experience with Linux-based production troubleshooting including CPU/memory/disk/network, Java and Python.
• Familiarity with observability tools (Splunk, Prometheus, OpenTelemetry, Dynatrace).
• Experience building RCA/AIOps tooling or contributing to related tooling.

Skills

Production ML

LLM systems in production

GenAI/LLM engineering

RAG embeddings

LangGraph

LangChain

Semantic Kernel

Tools guardrails

Human-in-the-loop auditing

Evaluation metrics

AIOps

Time-series anomaly detection

Python production code

Pandas

NumPy

scikit-learn

PyTorch

TensorFlow

Linux troubleshooting

Java troubleshooting

Python troubleshooting

Observability tools

Splunk

Prometheus

OpenTelemetry

Dynatrace

RCA/AIOps tooling

Job description

Cognizant is hiring an AI/AIOps Engineer to design, build, and productionize AI/ML and LLM systems. You will work on GenAI/LLM engineering including RAG, embeddings, and tool-driven automation with guardrails and human approvals.

The role requires hands-on production Python code, ML libraries, and strong troubleshooting across Linux, Java, and Python environments, with observability and RCA/AIOps tooling experience. This is a growth-focused position in a dynamic team.