Sr ML Engineer (Remote, Contract) [HR216] (PK)

Smart Working is seeking a Senior Machine Learning Engineer with extensive experience in Applied AI, Machine Learning, and MLOps to help build and modernize an AI platform. This role focuses on productionizing, deploying, evaluating, and operating ML/AI systems, combining backend engineering with advanced AI capabilities. You will work on enhancing existing NLP and generative AI models, developing new ML components, and creating robust infrastructure that supports seamless integration, monitoring, and deployment of machine learning models. This position offers the opportunity to contribute to a remote-first company that values community, growth, and well-being while working on impactful, long-term projects.

Key Responsibilities
- Refactor, modernize, and productionize existing ML models and Applied AI solutions, including NLP and generative AI systems.
- Develop new ML components and standardize existing models into modular, production-ready units.
- Build production ML applications and supporting services primarily using Python.
- Design and maintain reliable ML pipelines covering model integration, evaluation, deployment, and operation.
- Engineer resilient ML workflows with retry logic, error handling, and repeatable execution.
- Automate model evaluation pipelines using golden datasets with defined quality and performance thresholds.
- Evaluate models across various use cases, including generative AI and classification, applying appropriate metrics.
- Implement guardrails to assess grounding, hallucinations, and quality of generative AI outputs.
- Apply Applied AI techniques such as Retrieval-Augmented Generation (RAG) where relevant.
- Design provenance mechanisms for models, prompts, and input data to ensure auditability and reproducibility.
- Build infrastructure supporting shadow testing, A/B testing, fallback strategies, and kill switches for safe deployment.
- Support labeling, curation, and continuous improvement of golden datasets used for evaluation.
- Develop human-in-the-loop feedback pipelines to capture corrections and improve datasets.
- Integrate third-party AI APIs and develop adapter/API interfaces.
- Implement observability and telemetry to monitor model behavior, errors, compute costs, token usage, and latency.
- Provide backend engineering support to integrate ML components reliably into broader applications.
- Support both batch and real-time ML workloads as the platform evolves.

Required Qualifications
- Minimum 6 years of professional AI/Machine Learning experience with proven production deployment.
- At least 5 years of professional MLOps experience.
- Over 2 years of real Applied AI experience beyond experimentation or personal projects.
- Strong proficiency in Python, which is the core programming language for this role.
- Demonstrated experience in productionizing and deploying AI/ML models and applications.
- Deep understanding of Applied AI/ML and MLOps, beyond research or experimental work.
- Hands-on expertise in model evaluation and defining quality/performance criteria for production systems.
- Experience with generative AI and large language models (LLMs), including evaluation of grounding and hallucination issues.
- Practical knowledge of RAG and other Applied AI techniques.
- Proven ability to build and operate ML pipelines and production architectures.
- Skilled in designing reliable ML workflows with error handling and retry mechanisms.
- Experience using golden datasets for model evaluation and quality gating.
- Ability to build observable ML systems with logging, monitoring, and telemetry.
- Understanding of model and data provenance, auditability, and reproducibility.
- Experience implementing safe deployment practices, including testing and fallback mechanisms.
- Strong backend engineering skills to build APIs, integrations, and production-ready services around ML capabilities.
- Track record of solving real-world production ML challenges related to reliability, deployment, integration, evaluation, or performance.
- Familiarity with governance, compliance, and safeguards for sensitive data and AI-generated outputs.

Preferred Qualifications and Benefits
- Experience with FastAPI for building Python-based ML APIs.
- Exposure to Argo Workflows or similar DAG-based orchestration frameworks.
- Familiarity with Docker and Kubernetes.
- Experience working with major cloud platforms such as AWS, Azure, or GCP.
- Multi-cloud or cloud-agnostic application development experience.
- Knowledge of TypeScript and/or Go.
- Production experience with speech-to-text or transcription models.
- Experience in real-time ML applications.
- Background in traditional NLP models, transformer architectures, encoders, and decoders.
- Experience integrating external AI providers like OpenAI or Claude.

Smart Working is committed to using AI tools to assist in the hiring process, including resume analysis and application review, but all final hiring decisions are made by human recruiters. This role offers a chance to join a highly rated remote-first workplace that supports your professional growth and well-being in a collaborative environment.