Remote Senior ML Engineer – Applied AI & MLOps

Lever, Inc.; United States; Remote; USD 170,000 - 210,000; Full time

Job summary

Smart Working is seeking a Senior ML Engineer to build and modernise an AI platform, focusing on Applied AI, MLOps and backend engineering. The role expects deep experience in productionising ML models and building scalable ML services in Python.

You will work on production ML pipelines, evaluation, guardrails for generative AI, and integration with third-party APIs, while ensuring observability and governance across AI workloads.

Qualifications

• 6+ years of professional AI/ML 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 defining quality criteria.
• Experience with generative AI/LLMs and grounding/hallucination considerations.
• Hands-on understanding of RAG and Applied AI techniques.
• Experience building and operating ML pipelines and production ML architectures.
• Experience designing reliable ML workflows with error handling and retries.
• Experience with golden datasets for evaluation.
• Observability and telemetry for ML systems.
• Understanding of model/data provenance, auditability and reproducibility.
• Production deployment safeguards and fail-safe practices.
• Backend engineering experience to build APIs and integrations.
• Experience solving real production ML problems.
• Familiarity with governance and safeguards for sensitive data.

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 models into production-ready modules.
• Develop production ML applications and supporting services using Python.
• Build and maintain ML pipelines covering model integration, evaluation, deployment and operation.
• Engineer resilient ML workflows with retry logic and error handling.
• Design and automate model evaluation pipelines with golden datasets and quality thresholds.
• Evaluate models using appropriate metrics for outputs (generative AI, classification, etc).
• Implement guardrails and evaluation mechanisms for grounding and hallucination in outputs.
• Apply Applied AI techniques including RAG where appropriate.
• Design mechanisms for model, prompt and input-data provenance.
• Build infrastructure for shadow testing, A/B testing, fallback strategies and kill switches.
• Support labeling and curation of golden datasets for evaluation.
• Create human-in-the-loop feedback pipelines to improve datasets.
• Integrate third-party AI APIs and adapt interfaces.
• Implement observability and telemetry for model behavior, errors, costs, latency.
• Contribute backend engineering for ML components integration.
• Support batch and real-time ML workloads as the platform evolves.

Skills

Python

ML Engineering

MLOps

NLP

Generative AI

RAG

Model Evaluation

Backend APIs

Observability

Cloud & DevOps

Tools

Docker

Kubernetes

FastAPI

Argo Workflows

Go

TypeScript

AWS/Azure/GCP

Job description

Smart Working is seeking a Senior ML Engineer to build and modernise an AI platform, focusing on Applied AI, MLOps and backend engineering. The role expects deep experience in productionising ML models and building scalable ML services in Python.

You will work on production ML pipelines, evaluation, guardrails for generative AI, and integration with third-party APIs, while ensuring observability and governance across AI workloads.