Senior AI/Machine Learning Engineer - Remote - Contract

Senior AI and Machine Learning Engineer-Remote Contract Role
Remote contract opportunities for engineers who have owned production ML systems and can work directly with enterprise stakeholders.
Role: Senior AI and Machine Learning Engineer
Openings: Two separate contract roles:
a. approximately 20 hours per week (can co-exist with your existing engagement)
or
c. 40 hours per week
Engagement: Contract
Work arrangement: Remote; availability for agreed client meetings during US business hours
Start date: To be discussed
Rate: Hourly rate based on experience
Contract Options
20 hours per week: A flexible contract role that can coexist with another engagement, provided the agreed meetings and delivery commitments are met.
40 hours per week: A dedicated contract role requiring 40 hours of availability each week and the ability to prioritize this engagement during agreed working hours.
Role Description
Own the design, development, deployment, and improvement of AI and ML systems that support enterprise decisions. Build data pipelines, models, and agent workflows; measure real outcomes; and use those outcomes to guide model changes. Work with business and technical stakeholders to explain decisions, manage risks, and move delivery forward.
Responsibilities
Build the decision loop: Ingest and structure enterprise data, score or route decisions using ML models, rules, and agents, and capture outcomes as feedback.
Own the model lifecycle: Define evaluation criteria and ground truth; train, test, deploy, monitor, and tune models. Use shadow, canary, or A/B rollout patterns when appropriate.
Engineer production systems: Develop reliable Python services, data pipelines, and integrations. Address data quality, observability, failure handling, and performance.
Apply modern AI: Build and evaluate RAG, prompt-based, and agentic workflows. Use LLMs to identify patterns and propose changes, with human review where needed.
Work with clients: Lead technical discussions, ask targeted questions, explain model results and tradeoffs to business stakeholders, and document decisions.
Required Skills
Production experience: At least 3 years of ML engineering or applied AI experience with systems deployed and maintained in production.
Python and ML: Strong Python skills and practical experience with tools such as scikit-learn, XGBoost, or LightGBM, alongside LLM APIs and frameworks.
Enterprise data: Experience designing data pipelines and assembling usable ground truth from operational outcomes.
Evaluation and deployment: Experience measuring model quality, monitoring drift or performance, and deciding when to retrain or revise a model.
Applied AI: Experience with RAG, prompt engineering, evaluation harnesses, fine-tuning, or agent orchestration.
Communication: Ability to explain technical choices and model behavior to domain experts and senior business stakeholders.
Preferred Experience
Salesforce, Agentforce, Data Cloud, Einstein, Apex, or Flow; financial services or another regulated industry; versioned rule or policy deployment; consulting or client-facing applied AI delivery.
Working Style
We are looking for someone who can own work from an ambiguous business problem through a working production solution. The engineer should identify gaps early, recommend a practical next step, communicate progress and risks without prompting, and collaborate constructively with a lean team. Client meetings require clear explanations and a professional presence on video. Scheduling outside agreed meetings depends on the selected contract role.
Next Steps
Please share your resume, LinkedIn profile, desired hourly rate, which of the two contract roles you are available for, and your ability to attend meetings during US business hours. We will first review your experience and schedule a short introductory call.
Interview Process
Candidates will be asked to record a 2–3 minute video explaining how they would evaluate a decision model. More details will be provided. And this will be followed by an interview

Work arrangement
Yes