Senior 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 (containers, CI/CD, Dev Sec Ops ), 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.

• 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, autonomous/semi-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.

• 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.
• Build and deploy models and pipelines primarily on AWS (e.g., Sage Maker, Lambda, S3, ECS/EKS, Bedrock); working knowledge of GCP 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, Type Script, 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 Dev Sec Ops  principles across the ML lifecycle: security scanning, secrets management, infrastructure as code, and automated compliance checks.

• 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.

• Strong hands-on experience building and deploying ML solutions on AWS.
• Proven experience with LLMs, including OpenAI models/APIs, and current knowledge of…