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, autonomous/semi-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.

Mandatory Skills:

• Strong hands-on AWS ML/AI experience.
• LLMs, OpenAI models/APIs, and current AI/LLM model knowledge.
• Hands-on Agentic AI and multi-agent orchestration.
• RAG, embeddings, vector databases, and semantic retrieval.
• Strong data exploration, quality, and feature engineering experience.
• Python and Java.
• React/TypeScript or Java-based UI development.
• Docker/containers and GPU compute.
• CI/CD for ML/AI workloads.
• DevSecOps practices.
• Production ML/AI support and incident response.
• Model governance, monitoring, drift detection, and retraining.
• HITL/HOTL workflow design.
• AI vs. traditional model selection judgment.
• Model testing and evaluation.
• Automated test case development.
• AI governance, security, and guardrails.
• Familiarity with PyTorch, TensorFlow, Hugging Face, LangChain, LlamaIndex, or similar frameworks.

Preferred

:

• GCP and Azure ML/AI experience.
• Responsible AI toolkits.
• AWS ML/AI or relevant cloud certifications