ML Engineer

Job Title: ML EngineerJob DetailsWhat You Will Own
End-To-End Ml Lifecycle Across Real Products. Data Ingestion Feature Design Model Selection Training Deployment Monitoring Iteration. No Handoffs.
Production-Grade Ml Systems Built With Pytorch Or Tensorflow With Attention To Latency Reliability Cost And Failure Modes.
Applied Genai And Llm Work Where It Creates Measurable Value. Fine-Tuning Rag Prompt Orchestration Evaluation And Guardrails. No Hype-First Work.
Mlops Foundations. Model Versioning Ci,Cd Automated Testing Deployment Pipelines Serving Layers Monitoring And A,B Experimentation.
Tight Partnership With Product Engineering And Data. Translate Fuzzy Business Problems Into Tractable Ml Solutions And Quantify Impact.
Technical Leadership. Code Reviews Model Reviews Mentoring And Raising The Bar For Ml Engineering Discipline.
Incident Ownership. Debug Production Failures Data Drift Performance Regressions And Bias Issues Calmly And Decisively.

Required Profile
7+ Years Of Hands-On Ml Engineering With Clear Senior-Level Ownership Of Production Systems.
Strong Academic Grounding Or Equivalent Applied Depth In Machine Learning Computer Science Or Related Fields.
Expert Python. Deep Familiarity With Pytorch Preferred. Tensorflow Acceptable.
Demonstrated Experience Deploying Maintaining And Scaling Ml Models In Production Environments.
Solid Cloud Experience Across Aws Gcp Or Azure. Comfort With Spark Sql Docker Kubernetes.
Strong Grasp Of Ml Fundamentals. Model Architectures Optimization Tradeoffs Evaluation Design Experimentation Rigor.
Clear Written And Verbal Communication. Able To Explain Complex Systems Without Theatrics.