Forward Deployed Engineer - LLMOps

Systems Limited; Lahore; On-site; PKR 4,000,000 - 7,000,000; Full time

Job summary

Systems Limited is seeking an experienced MLOps Engineer to own production serving for LLM/GenAI workloads in Lahore, Pakistan. You will scale inference infra, manage caching, and optimize token costs while ensuring reliable operations for client projects.

You will implement observability pipelines, model routing, and rollout strategies, collaborating with GenAI engineers to maintain production readiness and cost governance for evolving workloads.

Qualifications

• 4–6 years in platform/MLOps engineering with hands-on LLM/GenAI production experience.
• Deep understanding of LLM inference economics including token costs, batching, caching, and routing.
• Experience with LLM observability tooling (tracing, eval pipelines, prompt/version management).
• Familiarity with multiple hosting platforms and cost/performance tradeoffs (Azure AI Foundry, AWS Bedrock, Vertex AI, self-hosted options).
• Experience building canary/rollback strategies for probabilistic systems.
• Calm under pressure during live incidents affecting client-facing systems.

Responsibilities

• Own production serving and scaling for LLM/agentic workloads (inference infra, load balancing, caching)
• Monitor and control inference cost — token usage, retry/loop cost, model routing decisions
• Build observability for LLM-specific failure modes: hallucination rate, latency spikes, prompt drift
• Manage model/version rollout strategy (canary releases, fallback models, A/B testing)
• Own incident response for LLM/agent production issues
• Partner with GenAI Engineers and Agentic AI Architects on production-readiness reviews
• Explain token-cost dynamics to client finance/business stakeholders
• Collaborate closely with GenAI Engineers without needing a hard line between build and run
• Support the practice in setting cost governance policy for LLM workloads

Skills

MLOps engineering

LLM production

Cost optimization

Observability tooling

Model routing

Tools

Azure AI Foundry

AWS Bedrock

Vertex AI

vLLM

TGI

Job description

ABOUT

Owns production operations for LLM and agentic workloads — serving, cost, and observability for a fundamentally less predictable class of system than classical ML.

KEY RESPONSIBILITIES

• Own production serving and scaling for LLM/agentic workloads (inference infra, load balancing, caching)
• Monitor and control inference cost — token usage, retry/loop cost, model routing decisions
• Build observability for LLM-specific failure modes: hallucination rate, latency spikes, prompt drift
• Manage model/version rollout strategy (canary releases, fallback models, A/B testing)
• Own incident response for LLM/agent production issues
• Partner with GenAI Engineers and Agentic AI Architects on production-readiness reviews
• Explain token-cost dynamics to client finance/business stakeholders
• Collaborate closely with GenAI Engineers without needing a hard line between build and run
• Support the practice in setting cost governance policy for LLM workloads

REQUIREMENTS & SKILLS

• 4–6 yrs platform/MLOps engineering with hands-on LLM/GenAI production experience
• Deep understanding of LLM inference economics — token costs, batching, caching, model routing
• Experience with LLM observability tooling (tracing, eval pipelines, prompt/version management)
• Familiarity with multiple model hosting platforms and their cost/performance tradeoffs — Microsoft Azure AI Foundry, AWS Bedrock, and Google Vertex AI, plus self-hosted open-source options (vLLM, TGI) as a good-to-have
• Experience building canary/rollback strategies for probabilistic systems
• Comfortable with the higher unpredictability of agentic workloads vs. classical ML serving
• Cost-conscious communicator — can explain a token-cost blowup to a client's finance stakeholder
• Collaborates closely with GenAI Engineers without needing a hard line between “build” and “run”
• Calm under pressure during live incidents affecting client-facing systems