Inviting applications for the role of SeniorPrincipal consultant GenAI LLM Ops Engineer!

We're looking for an LLM Ops Engineer to build, operate, and scale our Large Language Model platforms and applications. You'll own the end-to-end operational lifecycle-from model deployment and orchestration to reliability, observability, safety, cost optimization, and performance tuning. You'll partner closely with Data Science, MLOps, Security, and Product teams to deliver robust, compliant, and efficient LLM-powered experiences.

Responsibilities

Architect secure, reusable, and modular infrastructure-as-code (IaC) frameworks for GenAI and LLM operations

Design, deploy, and maintain LLM serving infrastructure (e.g., Azure OpenAI, self-hosted OSS models, vector databases).

Implement model orchestration (routing, ensemble strategies, fallbacks, retries, cache layers).

Build CI/CD pipelines for prompt catalogs, model configurations, guardrails, and evaluation suites.

Define and track LLM-specific SLOs (latency, response quality, safety violations, hallucination rate).

Implement telemetry (traces, logs, metrics, prompt/response analytics) and A/B experiments.

Establish alerting & incident response playbooks.

Lead the development and standardization of CI/CD pipelines for AI/ML model deployment

Ensure security, privacy, and regulatory compliance (data residency, consent, auditability).

Manage prompt governance (versioning, approval workflow, change logs, rollback).

Define and enforce best practices for model versioning, governance and lifecycle management

Troubleshoot and resolve issues related to LLM deployment, scaling, and performance

Stay updated with advancements in MLOps, LLMs, and GenAI technologies

Qualifications we seek in you!

Minimum Qualifications

Bachelor's degree in Computer Science, Engineering, or a related field

Proven experience in MLOps, DevOps, or AI/ML infrastructure roles

Hands-on experience with CI/CD pipelines, containerization, and orchestration tools (e.g., Docker, Kubernetes)

Proficiency in scripting and programming languages such as Python, Bash, or similar

Experience with cloud platforms (AWS, Azure, GCP) and infrastructure-as-code tools (Terraform, CloudFormation)

Strong understanding of machine learning model lifecycle management and operationalization

Knowledge of data privacy, security, and compliance standards in AI/ML environments

Preferred Qualifications,

. Master's degree in a relevant field
. Experience with large language models (LLMs) and generative AI frameworks
. Familiarity with monitoring, logging, and observability tools for AI/ML workloads
. Experience collaborating with cross-functional teams in enterprise environments
. Excellent problem-solving, communication, and documentation skills
. Prior experience mentoring or leading technical teams

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