Sr. Machine Learning Engineer (Remote, Contract) [HR216] (PK)
# Sr. Machine Learning Engineer (Remote, Contract) [HR216] (PK) **Smart Working** · Pakistan · `Remote` · `Contractor` 💼 **Уровень роли:** `Senior` 🕒 **Статус:** *Опубликовано: сегодня* · *Источник: Himalayas (JSON API)* --- ### Top Skills & Match 🎯 **Ключевой стек роли:** `[Machine-Learning-Engineer]` `[Senior-ML-Engineer]` `[MLOps-Engineer]` `[AI-Engineer]` `[Applied-AI-Engineer]` `[Remote-Machine-Learning-Engineer]` `[Senior-AI-ML-Engineer]` --- ### About the Role About Smart Working At Smart Working , we believe your job should not only look right on paper but also feel right every day. This isn’t just another remote opportunity — it’s about finding where you truly belong, no matter where you are. From day one, you’re welcomed into a genuine community that values your growth and well-being. Our mission is simple: to break down geographic barriers and connect skilled professionals with outstanding global teams and products for full-time, long-term roles. We help you discover meaningful work with teams that invest in your success, where you’re empowered to grow personally and professionally. Join one of the highest-rated workplaces on Glassdoor and experience what it means to thrive in a truly remote-first world. About the Role We are seeking a Senior ML Engineer with strong experience in Applied AI, Machine Learning and MLOps to build and modernise an AI platform. The role combines Applied AI, MLOps and backend/platform engineering, with a strong focus on productionising, deploying, evaluating and operating ML/AI systems. You will build new ML capabilities, modernise existing NLP and generative AI systems, and create reliable, observable infrastructure that makes models easier to integrate, evaluate, monitor and deploy. Responsibilities - 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 covering model integration, evaluation, deployment and operation. - Engineer resilient ML workflows with appropriate retry logic, error handling and repeatable execution. - Design and automate model evaluation pipelines using golden datasets and appropriate quality and performance thresholds. - Evaluate different types of models using metrics appropriate to their outputs, including generative AI, classification and other ML use cases. - Implement appropriate guardrails and evaluation mechanisms to assess grounding, hallucinations and quality of generative AI outputs. - Apply Applied AI techniques, including RAG , where appropriate to the ML capabilities being developed. - Design mechanisms for model, prompt and input-data provenance to support auditability and reproducibility. - Build infrastructure supporting shadow testing, A/B testing, fallback strategies and kill switches for safe ML deployment. - Support the labelling, curation and ongoing development of golden datasets used for model evaluation. - Build structured human-in-the-loop feedback pipelines to capture reviews and corrections and improve ML datasets. - Integrate third-party AI APIs and build appropriate adapter/API interfaces. - Implement observability and telemetry covering model behaviour, errors, compute costs, token usage and latency. - Contribute backend engineering capability required to integrate ML components reliably into the wider application. - Support both batch and real-time ML workloads as the platform develops. 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 , working with AI/ML capabilities beyond experimentation or personal projects. - 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 both Applied AI/ML and MLOps , rather than experience limited solely to model research or experimentation. - Strong hands-on experience with model evaluation and defining appropriate quality/performance criteria for production ML systems. - Experience working with generative AI/LLMs and understanding evaluation considerations such as grounding and hallucination. - 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 appropriate error handling, retry mechanisms and repeatable execution. - Experience working with golden datasets and using them for model evaluation and quality gating. - Experience building observable ML systems using appropriate logging, monitoring and telemetry. - Understanding of model/data provenance, auditability and reproducibility. - Experience implementing safe production deployment practices for ML systems, including appropriate 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, including reliability, deployment, integration, evaluation or performance challenges. - Familiarity with governance, compliance and safeguards relating to sensitive data and AI-generated outputs. Nice to Have - Experience with FastAPI for building Python-based ML APIs. - Exposure to Argo Workflows or similar DAG-based orchestration frameworks. - Experience with Docker and Kubernetes . - Experience working with one or more major cloud platforms: AWS, Azure or GCP . - Multi-cloud or cloud-agnostic application experience. - Experience or understanding of TypeScript and/or Go . - Production experience with speech-to-text or transcription models . - Experience working with real-time ML applications . - Experience with traditional NLP models, transformer-based models, encoders and decoders. - Experience integrating external models/providers such as OpenAI or Claude . Originally posted on Himalayas
- Machine-Learning-Engineer
- Senior-ML-Engineer
- MLOps-Engineer
- AI-Engineer
- Applied-AI-Engineer
- Remote-Machine-Learning-Engineer
- Senior-AI-ML-Engineer
Наблюдалась 2026-10-09, впервые 2026-10-09, источник — Himalayas (JSON API).