Machine Learning Engineer

About This RoleAI processing…

Key Responsibilities

• 1
  Partner with product, UX, and technical stakeholders to analyze business problems, clarify requirements, define scope, and translate them into measurable ML problem statements.
• 2
  Design, implement, and maintain scalable, enterprise-grade ML solutions in production.
• 3
  Build reproducible ML workflows for data preparation, training, evaluation, and inference using modern orchestration and MLOps tooling.
• 4
  Implement monitoring and evaluation frameworks to continuously improve data quality, model performance, latency, and cost through feedback loops.
• 5
  Partner cross-functionally with Product, Data Science/ML, Engineering, and Security to deliver resilient, scalable, and compliant ML-powered services.
• 6
  Demonstrate end-to-end systems understanding and articulate the “why” behind model and system design choices.
• 7
  Own operational excellence: SLAs, on-call, incident response, customer feedback triage, and blameless post-mortems.
• 8
  Drive engineering excellence via AI-assisted SDLC, code reviews, automated testing, MLOps best practices, knowledge-sharing, and mentoring.
• 9
  Actively adopt AI-assisted practices to improve implementation and collaboration efficiency.

Requirements

• Strong foundation in ML/AI (statistics, probability, optimization) with the ability to apply these concepts to real-world problems.
• 5+ years of experience building, deploying, and operating data and ML systems in production.
• Proficient in Python, Java, and SQL; strong software engineering fundamentals (system design, testing, version control, code reviews).
• Hands-on experience with workflow orchestration and data pipelines (e.g., Airflow, Kubeflow) and cloud data platforms/storage (e.g., SageMaker Feature Store, Snowflake, DynamoDB, OpenSearch).
• Experience with the ML lifecycle and MLOps tooling (e.g., MLflow, Metaflow, SageMaker; LLM/agent frameworks such as LangChain/LangGraph; model evaluation/observability tools such as Galileo or similar).
• Working knowledge of containerization and cloud infrastructure, including Docker and Kubernetes, GitOps/CI/CD tools (e.g., Argo CD), and at least one major cloud platform (AWS, GCP, or Azure).
• Understanding of data modeling and scalable systems, including distributed computing and streaming frameworks (e.g., Spark/EMR, Flink, Kafka Streams); familiarity with GPU-based implementation is a plus.
• Demonstrated ability to ramp up quickly and operate effectively in new application/business domains.
• Strong written and verbal communication skills: able to document and present designs and decisions, and comfortable giving/receiving feedback in an Agile environment.
• Familiarity with ML problem areas and techniques, including recommendation systems (e.g., graph-based approaches, two-tower models), time-series modeling (classical and deep learning), representation learning (e.g., embeddings), anomaly detection, and causal inference.
• Practical experience with LLMs and generative AI workflows, including foundation model fine-tuning, RAG, and vector databases.
• Evidence of technical leadership/impact, such as contributions to open-source data/ML projects and/or published technical presentations, blog posts, papers, or research.
• Domain experience (plus) in communications, marketing automation, or customer engagement analytics.
• Familiarity with AI-assisted development tools (e.g., Claude, GitHub Copilot/Codex, Cursor, etc.).
• Advanced degree preferred (M.S. or Ph.D.) in a relevant field.

Perks & Benefits