Early-career ML systems engineer with nearly 3 years driving high-impact inference optimization: vLLM migrations, quantization strategies (INT8/AWQ), and autoscaling across GPU fleets. Balances hands-on infrastructure work (Kubernetes, AWS, Prometheus) with deep model-serving expertise while completing an M.S. in Computer Science full-time.
Route to LLM Inference Engineer, ML Platform Engineer, ML Systems Engineer teams building high-throughput services in the candidate's core stack.
LLM inference optimization (vLLM, quantization, batching, KV-cache tuning). Production ML systems on Kubernetes with GPU fleet management and autoscaling. Performance profiling and latency reduction (PyTorch Profiler, request tracing, observability). End-to-end ML deployment (model evaluation, load testing, on-call reliability).
Full-Stack Engineer, Inference/Model Serving Specialist, ML Systems Engineer, Infrastructure/DevOps Engineer.
Remote-first. Hybrid considered for high-alignment local roles. Based in Not specified, US.
Targeting $200K+. Currently: Software Engineer, Inference.
Recruiter screen → hiring-manager review → technical conversation around INT8 quantization deployment, Queue-depth autoscaling on EKS, Root Stress Detection System (IoT + ML).
Ahmed's work in Full-Stack Engineer, Inference/Model Serving Specialist, ML Systems Engineer translates directly into adjacent domains with similar architecture, scale, and reliability needs.
This reusable proof index maps common senior/staff engineering requirements to candidate proof. Role-specific addenda extend — not replace — this base matrix.
Requirement: Deliver on int8 quantization deployment at production scale.
Proof: 30% GPU memory reduction, freed capacity for additional workload
Requirement: Deliver on queue-depth autoscaling on eks at production scale.
Proof: 20% reduction in idle GPU-hours across ~40-GPU fleet
Requirement: Deliver on root stress detection system (iot + ml) at production scale.
Proof: 8% prediction accuracy improvement
Requirement: Deliver on tokenization bottleneck elimination at production scale.
Proof: 10% end-to-end latency improvement via async worker pool
Requirement: Deliver on vllm migration and tuning at production scale.
Proof: 1.6× throughput increase, 25% p99 latency reduction
Requirement: Ship work that requires llm inference optimization (vllm, quantization, batching, kv-cache tuning).
Proof: Demonstrated at prior roles.
30% GPU memory reduction, freed capacity for additional workload
20% reduction in idle GPU-hours across ~40-GPU fleet
8% prediction accuracy improvement
10% end-to-end latency improvement via async worker pool
1.6× throughput increase, 25% p99 latency reduction
Mid — role fit: adjacent.
Mid — role fit: primary.
Mid — role fit: primary.
github.com/AhmedMujtaba999 — public repositories, contribution activity, and open source project surfaces.
Hosted candidate portfolio with positioning, proof points, and conversion kit surface for recruiter routing.
linkedin.com/in/ahmedmujtabaali
Professional profile and recruiter connection path.
This kit is designed to survive internal forwarding. The fastest next step is a recruiter screen, hiring-manager review, referral handoff, or direct call.
Primary fit: LLM Inference Engineer, ML Platform Engineer, ML Systems Engineer.
Fastest way to reach Ahmed — grab a slot on their calendar.
Persistent candidate surface for routing, resume, and proof surfaces.
Professional profile and recruiter connection path.
Public engineering surface, repositories, and OSS project path.
Additional candidate-owned portfolio and technical writing surface.
Ahmed Ali is a Not specified, US-based LLM Inference Engineer. Early-career ML systems engineer with nearly 3 years driving high-impact inference optimization: vLLM migrations, quantization strategies (INT8/AWQ), and autoscaling across GPU fleets. Balances hands-on infrastructure work (Kubernetes, AWS, Prometheus) with deep model-serving expertise while completing an M.S. in Computer Science full-time. Best fit: LLM Inference Engineer, ML Platform Engineer, ML Systems Engineer.