Senior Machine Learning Engineer – Contract

🔥 0 minutes ago

🇵🇰 Pakistan – Remote

⏱ Part Time

🟠 Senior

🤖 Machine Learning Engineer

👻 Ghost score 15%

What it means: how likely this posting is sitting open without active hiring.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

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Smart Working

WebsiteLinkedInAll Job Openings

51 - 200 employees

💼 Consulting

🏥 Healthcare

📣 Marketing

Consulting • Healthcare • Marketing

Smart Working is a recruitment service specializing in sourcing and providing top-tier software developers from around the world to meet the needs of businesses. With a robust vetting process that includes technical assessments and background checks, Smart Working ensures that clients receive highly skilled developers adept in various programming languages and frameworks. The company focuses on flexible and remote hiring solutions, allowing businesses to efficiently scale their development teams while benefiting from significant cost savings.

📋 Description

• 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 standardised, production-ready modular components • Develop production ML applications and supporting services primarily using Python • Build and maintain reliable ML pipelines for 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/performance thresholds • Evaluate generative AI, classification and other ML models using appropriate metrics • Implement guardrails and evaluation mechanisms for grounding, hallucinations and generative AI output quality • Apply Applied AI techniques, including RAG • Design model, prompt and input-data provenance mechanisms for auditability and reproducibility • Build infrastructure for shadow testing, A/B testing, fallback strategies and kill switches • Support labelling, curation and ongoing development of golden datasets • Build human-in-the-loop feedback pipelines to capture reviews and corrections • Integrate third-party AI APIs and build adapter/API interfaces • Implement observability and telemetry for model behaviour, errors, compute costs, token usage and latency • Contribute backend engineering to integrate ML components into the wider application • Support batch and real-time ML workloads

🎯 Requirements

• 6+ years of professional AI/Machine Learning experience, with genuine production experience • 5+ years of professional MLOps experience • At least 2+ years of real Applied AI experience beyond experimentation or personal projects • Strong professional Python experience • 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 defining quality/performance criteria • Experience with generative AI/LLMs, including grounding and hallucination evaluation • 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, retry mechanisms and repeatable execution • Experience with golden datasets for model evaluation and quality gating • Experience building observable ML systems using logging, monitoring and telemetry • Understanding of model/data provenance, auditability and reproducibility • Experience implementing safe production deployment practices, including testing, fallback or fail-safe mechanisms • Sufficient backend engineering experience to build APIs, integrations and production-ready services around ML capabilities • Experience solving real production ML problems involving reliability, deployment, integration, evaluation or performance • Familiarity with governance, compliance and safeguards for sensitive data and AI-generated outputs • Nice to have: FastAPI • Nice to have: Argo Workflows or similar DAG-based orchestration frameworks • Nice to have: Docker and Kubernetes • Nice to have: AWS, Azure or GCP • Nice to have: Multi-cloud or cloud-agnostic application experience • Nice to have: TypeScript and/or Go • Nice to have: Speech-to-text or transcription models • Nice to have: Real-time ML applications • Nice to have: Traditional NLP models, transformer-based models, encoders and decoders • Nice to have: External model/provider integrations such as OpenAI or Claude

🏖️ Benefits

• Remote work arrangement • Genuine community focused on growth and well-being • Long-term role • Professional and personal growth opportunities