Data Scientist

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, 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 strong understanding of data, feature engineering, and model behavior.

•             Develop solutions using LLMs and generative AI, including OpenAI models/APIs.

•             Design and implement agentic AI solutions, including multi-step, tool-using, autonomosemi-autonomous agents.

•             Build and evaluate RAG solutions, including embeddings, vector search, semantic chunking, and retrieval strategies.

•             Determine when to use AI/LLM solutions versus traditional deterministic or statistical approaches.

•             Design human-in-the-loop (HITL) and human-on-the-loop (HOTL) workflows.

•             Establish measurable testing and evaluation criteria such as accuracy, precision/recall, drift, latency, cost, hallucination rate, and bias metrics.

•             Write and maintain automated test cases for model validation, including AI-assisted approaches for test coverage.

Operational Support & Model Lifecycle

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

•             Implement governance and monitoring frameworks to track performance, drift, bias, and usage.

•             Own model updates, including retraining, fine-tuning, versioning, and re-validation.

•             Implement logging, tracing, audit trails, model decision tracking, and lineage.

Cloud, Engineering & Front-End

•             Build and deploy solutions primarily on AWS, including SageMaker, Lambda, S3, ECS/EKS, and Bedrock.

•             Strong coding skills in Python and Java.

•             Build interactive front-end applications using React, TypeScript, or Java-based frameworks.

•             Containerize workloads using Docker and manage GPU-based compute.

•             Build and maintain CI/CD pipelines for ML/AI workloads.

•             Apply DevSecOps principles, including security scanning, secrets management, infrastructure as code, and automated compliance checks.

Governance, Security & Responsible AI

•             Ensure AI/ML solutions meet governance, legal, security, and regulatory requirements.

•             Implement guardrails for data privacy, bias mitigation, content safety, access controls, and prompt-injection defenses.

•             Ensure models and pipelines meet compliance requirements before production release.