Sr. Machine Learning 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 a cutting-edge 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 ensures models are easy to integrate, monitor, and maintain. This position offers the opportunity to join a highly-rated remote-first company that values growth, well-being, and meaningful work.

Key Responsibilities
- Refactor, modernize, and productionize existing ML models and Applied AI capabilities, including NLP and generative AI solutions.
- Build new ML components and re-engineer existing models into standardized, production-ready modular units.
- Develop production ML applications and supporting services primarily using Python.
- Construct and maintain reliable ML pipelines covering 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 defined quality and performance thresholds.
- Evaluate various model types using appropriate metrics for generative AI, classification, and other ML use cases.
- Implement guardrails and evaluation mechanisms to assess grounding, hallucinations, and quality of generative AI outputs.
- Apply Applied AI techniques such as Retrieval-Augmented Generation (RAG) where relevant.
- Design mechanisms for model, prompt, and input-data provenance to support auditability and reproducibility.
- Build infrastructure for shadow testing, A/B testing, fallback strategies, and kill switches to ensure safe ML deployment.
- Support labeling, curation, and ongoing development of golden datasets for model evaluation.
- Develop structured human-in-the-loop feedback pipelines to capture reviews and corrections, improving ML datasets.
- Integrate third-party AI APIs and create adapter/API interfaces as needed.
- Implement observability and telemetry to monitor model behavior, errors, compute costs, token usage, and latency.
- Contribute backend engineering expertise to integrate ML components reliably into the broader application.
- Support both batch and real-time ML workloads as the platform evolves.

Required Qualifications
- Minimum of 6 years of professional AI/Machine Learning experience, including genuine production deployment.
- At least 5 years of professional MLOps experience.
- 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.
- Proven track record of productionizing and deploying AI/ML applications and models.
- Deep understanding of Applied AI/ML and MLOps, beyond model research or experimentation.
- Hands-on experience with model evaluation and defining quality/performance criteria for production ML systems.
- Experience working with generative AI and large language models (LLMs), including evaluation of grounding and hallucination issues.
- Practical knowledge of RAG and other Applied AI techniques.
- Experience building and operating ML pipelines and production ML architectures.
- Skilled in designing reliable ML workflows with error handling, retry mechanisms, and repeatable execution.
- Familiarity with golden datasets and their use in model evaluation and quality gating.
- Experience developing observable ML systems with appropriate logging, monitoring, and telemetry.
- Understanding of model and data provenance, auditability, and reproducibility.
- Knowledge of safe production deployment practices, including testing, fallback, and fail-safe mechanisms.
- Strong backend engineering skills to build APIs, integrations, and production-ready services around ML capabilities.
- Experience solving real-world production ML challenges related to reliability, deployment, integration, evaluation, or performance.
- Awareness of governance, compliance, and safeguards related to 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 experience.
- Knowledge of TypeScript and/or Go.
- Production experience with speech-to-text or transcription models.
- Experience with real-time ML applications.
- Familiarity with traditional NLP models, transformer architectures, encoders, and decoders.
- Experience integrating external AI models/providers like OpenAI or Claude.

Smart Working embraces AI tools to assist in the recruitment process, such as reviewing applications and analyzing resumes. However, all final hiring decisions are made by human recruiters. Candidates interested in learning more about data processing during recruitment are encouraged to inquire further.