Data Scientist (local to MI)

ONSITE ROLE FROM DAY ONE. ONLY CONSIDERING LOCAL OR NEARBY STATES CANDIDATES.

Position Summary

We are seeking a highly technical Data Scientist with deep cloud experience, primarily AWS, to design, build, and operationalize machine learning and AI/LLM solutions. This role requires strong engineering discipline (containers, CI/CD, DevSecOps), current knowledge of generative and agentic AI, front-end delivery of model outputs, and a rigorous approach to governance, security, monitoring, and measurable model quality across the full model lifecycle.

Key Responsibilities

Model Development & AI/ML Engineering

Design, build, train, and validate machine learning models with deep understanding of the underlying data, feature engineering, and model behavior.

Develop solutions using LLMs and generative AI, including OpenAI modules/APIs, staying current with the latest AI/LLM model releases and capabilities.

Design and implement agentic AI solutions (multi-step, tool-using, autonomosemi-autonomous agents), understanding orchestration, memory, and tool-calling patterns.

Build and evaluate RAG (Retrieval-Augmented Generation) solutions, including semantic RAG architectures (embeddings, vector search, semantic chunking/retrieval strategies).

Exercise sound judgment on when to apply AI/LLM solutions vs. traditional deterministic or statistical approaches, and select the appropriate model type/size/architecture for a given problem.

Design for human-in-the-loop (HITL) and human-on-the-loop (HOTL) patterns appropriately, determining where human review, approval, or oversight is required in inference, retraining, or tuning workflows.

Establish clear, measurable testing and evaluation criteria for model builds (accuracy, precision/recall, drift, latency, cost, hallucination rate, bias metrics).

Write and maintain automated test cases for model validation, including using AI-assisted tools to generate and expand test coverage for model builds.

Operational Support & Model Lifecycle

Provide operational support for deployed models, including monitoring, incident triage, and troubleshooting of production ML/AI services.

Implement governance and monitoring frameworks around deployed models to track performance, drift, bias, and usage over time.

Own the model update lifecycle: retraining, fine-tuning, versioning, and periodic re-validation as data and business conditions evolve.

Use logging/chronicle-based tracing and audit trails to track model decisions, retraining events, and lineage over time.

Cloud, Engineering & Front-End

Build and deploy models and pipelines primarily on AWS (e.g., SageMaker, Lambda, S3, ECS/EKS, Bedrock); working knowledge of Google Cloud Platform and Azure is a plus.

Strong coding skills in Python and Java for model services, pipelines, and backend integration.

Build interactive front-end UI applications to present model outputs and insights using React, TypeScript, or Java-based frameworks.

Containerize model workloads using Docker/containers, and manage GPU-based compute for training and inference workloads.

Build and maintain CI/CD pipelines for model training, validation, and deployment.

Apply DevSecOps principles across the ML lifecycle: security scanning, secrets management, infrastructure as code, and automated compliance checks.

Governance, Security & Responsible AI

Maintain deep awareness of governance, legal, and security requirements applicable to AI/ML model development and data usage.

Design and implement guardrails in model development (data privacy, bias mitigation, content safety, access controls, prompt injection defenses for LLM/agentic systems).

Ensure models and pipelines meet organizational and regulatory compliance requirements prior to production release.

Required Skills & Qualifications (Mandatory)

Strong hands-on experience building and deploying ML solutions on AWS.

Proven experience with LLMs, including OpenAI models/APIs, and current knowledge of leading AI/LLM model families.

Hands-on experience building agentic AI systems (multi-agent orchestration, tool use, autonomous workflows).

Experience building RAG systems, including semantic RAG (embeddings, vector databases, semantic retrieval).

Deep understanding of data: exploration, quality, feature engineering, and its impact on model outcomes.

Strong coding proficiency in Python and Java.

Experience building front-end interactive applications (React, TypeScript, or Java-based UI) to surface model outputs to end users.

Practical experience with Docker/containers and GPU compute for training/inference.

Experience building and maintaining CI/CD pipelines for ML/AI workloads.

Working knowledge of DevSecOps practices applied to ML pipelines.

Experience providing operational support for production ML/AI systems, including monitoring and incident response.

Experience implementing model governance and monitoring (drift detection, performance tracking, periodic retraining/tuning cycles).

Demonstrated ability to design for human-in-the-loop / human-on-the-loop workflows for model oversight, retraining, and tuning.

Demonstrated judgment in model/technique selection, including when to use AI/LLM approaches vs. traditional methods.

Experience defining measurable testing/evaluation criteria for model performance and quality.

Experience writing automated test cases, including using AI-assisted approaches to generate test coverage for model builds.

Solid understanding of AI governance, legal, and security requirements, and experience embedding guardrails into model development.

Familiarity with ML/AI frameworks (e.g., PyTorch, TensorFlow, Hugging Face, LangChain/LlamaIndex or similar agentic/RAG frameworks).

Preferred Qualifications

Working knowledge of Google Cloud Platform and Azure ML/AI services.

Experience with responsible AI toolkits (bias/fairness testing, model explainability).

Certifications in AWS ML/AI or relevant cloud platforms.