AIML Engineer

Skill- AIML Engineer

Total Experience-3-7yrs

Mode - Hybrid

JD:

Key Responsibilities:

AI Ops & Observability

Design, implement, and operate AIdriven monitoring and observability solutions across application, infrastructure, and network layers.

Able to build AIbased anomaly detection for metrics such as traffic patterns, error rates, latency, and infrastructure capacity.

Integrate observability platforms such as Dynatrace, Splunk, OpenTelemetry, and custom metrics pipelines for AI monitoring and insights.

AIenabled alert reduction and error detection

Automation & Intelligent Operations

Develop AIpowered automation to reduce operational toil, manual triage, and repetitive tasks.

Implement alert reduction, intelligent correlation, and RCA automation for production incidents.

Enable selfhealing and autoremediation workflows where applicable.

Required Skills:

5+ years of experience in Python using common data science libraries such as scikit-learn and Databricks.

3+ years of experience applying NLP (transformers, gpt,etc) in a production setting using large amounts of data.

2-3 years of experience applying LLMs (RAG, vector db, embeddings) to solve real world problems.

Experience applying data science techniques in a commercial enterprise, including supervised, unsupervised learning, NLP, time-series forecasting and other statistical analyses.

Experience building APIs for the models.

Good experience with one of the cloud platforms (Azure, AWS or GCP). Having Azure cloud experience is more preferred.

Strong written & verbal communication skills; including the ability to present detailed analyses to a broad audience.

Organized self-starter, with drive and commitment; able to work with little supervision.

Analytical, quantitative, problem-solving, and critical thinking skills

Nice-to-Have:

Experience using vision and speech models in GenAI

Experience with AI/ML concepts applied to operations

Knowledge of SRE practices, incident management