Data Scientist (AWS Bedrock)

Job Title

Data Scientist (AWS Bedrock)

Location

Chicago, IL

About the Role

We are seeking a highly skilled Data Scientist with hands‑on experience in AWS Bedrock to design, build, and scale generative AI and machine learning solutions. The ideal candidate has a strong foundation in Python, applied ML, LLMs, and cloud‑native development with a focus on production‑grade models and prompt engineering.

Key Responsibilities

• Develop, fine-tune, and deploy generative AI/LLM solutions using AWS Bedrock, including foundation models such as Claude, Llama, and Titan.
• Build end‑to‑end ML workflows leveraging AWS services (S3, Lambda, SageMaker, Step Functions, API Gateway, DynamoDB, RDS, etc.).
• Design and implement prompt engineering strategies, evaluation frameworks, and model optimisation techniques.
• Integrate Bedrock‑powered AI capabilities into applications via APIs and SDKs.
• Collaborate with cross‑functional teams to identify business problems and translate them into scalable AI/ML solutions.
• Perform data preprocessing, feature engineering, statistical modelling, and experimentation.
• Develop scalable pipelines for model training, inference, and monitoring.
• Conduct A/B testing, model performance evaluations, and continuous improvement activities.
• Ensure adherence to security, compliance, and responsible AI best practices within AWS.
• Produce clear technical documentation, reports, and model explainability outputs.

Required Skills & Qualifications

• Bachelor's or Master's in Computer Science, Data Science, Engineering, Mathematics, or a related field.
• 3–7 years of experience as a Data Scientist or ML Engineer.
• Strong hands‑on expertise with AWS Bedrock, including provisioning, model selection, and orchestration.
• Advanced proficiency in Python, including libraries such as NumPy, pandas, scikit‑learn, PyTorch or TensorFlow.
• Experience building and deploying ML/LLM applications in AWS.
• Knowledge of vector databases (e.g., Pinecone, FAISS, OpenSearch) and RAG pipelines.
• Strong grasp of data modelling, statistics, NLP, and machine learning algorithms.
• Familiarity with CI/CD, MLOps, containerisation (Docker), and version control (Git).
• Strong problem‑solving abilities, analytical mindset, and communication skills.

Preferred Qualifications

• Experience fine‑tuning LLMs using SageMaker or custom training pipelines.
• Prior work with multimodal models, retrieval‑augmented generation (RAG), or agent‑based architectures.
• Certifications: AWS Solutions Architect, AWS Machine Learning Specialty, or equivalent.
• Experience integrating Bedrock with real‑time applications and microservices.

Seniority level

Mid‑Senior level

Employment type

Full‑time

Job function

Engineering and Information Technology

Industries

IT Services and IT Consulting

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