Senior ML Engineer — Remote-First AI Platform & MLOps Lead

Lever, Inc.; Karachi Division; Teletrabalho; PKR 33 241 000 - 55 402 000; Tempo integral

Resumo da oferta

Smart Working is seeking a Senior ML Engineer to build and modernise an AI platform, specializing in Applied AI, NLP and MLOps. You will productionise ML capabilities, deploy and evaluate models, and create observable infrastructure for scalable, reliable AI systems.

You’ll own end-to-end ML pipelines, integrate third-party AI APIs, and contribute to backend services that enable real-time and batch ML workloads while ensuring governance and safety in AI outputs.

Qualificações

• 6+ years of professional AI/Machine Learning experience
• 5+ years of professional MLOps experience
• At least 2+ years of real Applied AI experience
• Strong professional Python experience; Python is the core programming language for this role
• Proven experience productionising and deploying AI/ML applications and models
• Strong understanding of Applied AI/ML and MLOps
• Strong hands-on experience with model evaluation and quality criteria
• Experience with generative AI/LLMs and evaluation considerations
• Hands-on understanding of RAG and other Applied AI techniques
• Experience building and operating ML pipelines and production ML architectures
• Experience designing reliable ML workflows with error handling and retries
• Experience working with golden datasets for evaluation
• Experience building observable ML systems with logging, monitoring and telemetry
• Understanding of model/data provenance, auditability and reproducibility
• Experience implementing safe production deployment practices for ML systems
• Sufficient backend engineering experience to build APIs and integrations around ML capabilities
• Experience solving real production ML problems
• Familiarity with governance and safeguards relating to sensitive data and AI outputs

Responsabilidades

• Refactor, modernise and productionise existing ML models and Applied AI capabilities, including NLP and generative AI solutions.
• Build new ML components and re-engineer existing models into modular, production-ready components.
• Develop production ML applications and supporting services primarily using Python.
• Build and maintain reliable ML pipelines covering model integration, evaluation, deployment and operation.
• Engineer resilient ML workflows with retry logic, error handling and repeatable execution.
• Design and automate model evaluation pipelines using golden datasets and quality thresholds.
• Evaluate different model types using appropriate metrics for outputs (generative AI, classification, etc).
• Implement guardrails and evaluation mechanisms to assess grounding and hallucinations in generative AI outputs.
• Apply Applied AI techniques including RAG where appropriate to the ML capabilities.
• Design mechanisms for model, prompt and input-data provenance for auditability.
• Build infrastructure supporting shadow testing, A/B testing, fallbacks and kill switches for safe deployment.
• Support labelling, curation and development of golden datasets used for evaluation.
• Build human-in-the-loop feedback pipelines to improve ML datasets.
• Integrate third-party AI APIs and build adapters/interfaces.
• Implement observability/telemetry for model behavior, errors, costs, token usage and latency.
• Contribute backend engineering for reliable ML integration into the wider app.
• Support batch and real-time ML workloads as the platform evolves.

Conhecimentos

Python

Productionising ML

MLOps

Applied AI

NLP / Generative AI

Model evaluation

Back-end engineering

Observability / Telemetry

RAG techniques

ML pipelines

Golden datasets

Retry / error handling

Ferramentas

FastAPI

Argo Workflows

Docker

Kubernetes

AWS

Azure

GCP

Descrição da oferta de emprego

Smart Working is seeking a Senior ML Engineer to build and modernise an AI platform, specializing in Applied AI, NLP and MLOps. You will productionise ML capabilities, deploy and evaluate models, and create observable infrastructure for scalable, reliable AI systems.

You’ll own end-to-end ML pipelines, integrate third-party AI APIs, and contribute to backend services that enable real-time and batch ML workloads while ensuring governance and safety in AI outputs.