We're looking for a Senior Agentic AI Data Scientist to join MindBridge's Applied AI team. You'll design, build, and scale LLM-powered autonomous systems that reason, plan, and act across complex business workflows. Reporting to the AI Architect, you'll own the full lifecycle, from problem framing through deployment and governance. You'll partner closely with engineering and product teams to deliver production-grade agentic solutions.

Agentic System Design & Development

• Design and implement autonomous AI systems that reason, plan, and execute multi-step workflows toward business objectives
• Architect multi-agent systems (planners, tool-callers, reviewers, supervisors) using orchestration frameworks such as LangGraph, LangChain, AutoGen, or similar
• Build robust RAG pipelines, prompt strategies, and memory mechanisms to ground agent behavior in enterprise data
• Develop guardrails, escalation paths, and human-in-the-loop patterns for safe, explainable agent behavior
• Collaborate with the AI Architect to translate conceptual AI architectures into robust, scalable implementations

Data Science & Experimentation

• Analyze large, multi-source datasets to identify high-impact opportunities for agentic automation
• Build and maintain models that agents rely on (scoring, ranking, recommendation, policy models)
• Design and execute experiments comparing agentic approaches against baselines, using rigorous statistical methods to quantify business impact

Production Engineering & MLOps

• Build end-to-end pipelines for training, evaluation, deployment, and monitoring of agents and underlying models
• Implement comprehensive observability: tracing, logging, cost tracking, and performance dashboards for agent behavior
• Partner with platform teams to ensure scalability, reliability, and cost efficiency across cloud infrastructure, vector stores, and orchestration layers

Evaluation & Governance

• Define evaluation frameworks and metrics: task completion rates, hallucination/error rates, escalation frequency, safety incidents, and business outcomes
• Build offline evaluation datasets and online A/B tests for continuous improvement
• Contribute to AI governance practices including documentation, system cards, and audit trails

Required

• 5+ years in applied ML, data science, or ML engineering with production systems experience
• Strong Python proficiency and fluency with ML tooling (PyTorch, TensorFlow, NumPy, Pandas, or similar)
• Hands-on experience with LLMs and generative AI (prompt engineering, RAG, fine-tuning)
• Experience with agentic/orchestration frameworks or equivalent multi-step AI workflow development
• Practical MLOps experience (MLflow, Kubeflow, Airflow, Vertex AI, SageMaker, or similar)
• Solid software engineering fundamentals: version control, testing, CI/CD, code review

Preferred

• Production experience with autonomous LLM/agentic systems
• Knowledge of distributed systems and cloud platforms (AWS, GCP, Azure) with containerization (Docker, Kubernetes)
• Background in RAG architectures, reinforcement learning, or multi-agent coordination
• Experience with data platforms (warehouses, streaming systems, vector databases)
• Familiarity with AI governance and risk management in enterprise/regulated environments
• Track record of mentoring engineers and leading cross-functional initiatives

• You bridge the gap between experimental prototypes and hardened production systems
• You communicate complex AI concepts clearly to both technical and non-technical stakeholders
• You balance speed of iteration with robustness, security, and maintainability

Pay Range

The expected base salary range for this position is $152,000 to $195,000 and may be eligible for bonus awards. The determination of an applicant’s base salary within this range is based on the individual’s location, skills, experience and competencies, and unique qualifications.

Requirements Contingent on Employment

• Fulfill requirements necessary to obtain and clear a full background check