Data Scientist

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 GCP and Azure ML/AI services.

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

Certifications in AWS ML/AI or relevant cloud platforms.