资深机器学习工程师（远程，合同）[HR216]（PK）

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