Data Science/MLOps Engineer

TalentOla•Chicago, IL

1d•Onsite

About The Position

We are seeking a skilled Data Science/MLOps Engineer to join our team. This role involves designing, developing, and maintaining Python-based applications, data pipelines, and AI/ML solutions. You will be responsible for building, training, evaluating, and deploying machine learning and data science models for production use. A key part of this role will be developing and integrating Generative AI solutions using AWS Bedrock, including foundation model selection, prompt engineering, and inference orchestration. You will also design and manage AWS cloud infrastructure using Terraform, adhering to IaC best practices, and build scalable AI/ML and GenAI deployment architectures using AWS services. Additionally, you will develop and optimize data ingestion, processing, and analytics pipelines for structured and unstructured data, collaborate with cross-functional teams, implement CI/CD pipelines, monitoring, logging, and performance optimization, and ensure security, compliance, governance, and cost optimization of cloud and AI workloads. You will also mentor junior engineers, conduct code reviews, contribute to architectural decisions, and create technical documentation.

Requirements

Strong 6–10 years of overall software engineering experience.
Strong proficiency in Python for backend systems, data processing, and ML workflows.
Hands‑on experience in Data Science and Machine Learning, including feature engineering, model evaluation, and deployment.
Experience with ML frameworks such as Scikit‑learn, PyTorch, TensorFlow, or similar.
Strong experience with AWS services, including EC2, S3, Lambda, ECS/EKS, RDS, SageMaker, and AWS Bedrock.
Practical knowledge of AWS Bedrock for building and operationalizing Generative AI applications.
Hands‑on experience with Terraform for infrastructure provisioning and environment management.
Experience building cloud‑native, scalable, and highly available systems.
Solid understanding of data stores (SQL, NoSQL) and data processing architectures.
Familiarity with Docker, Kubernetes, and modern DevOps practices.
Strong problem‑solving, communication, and collaboration skills.

Nice To Haves

Prior experience with clinical, biomedical, or healthcare NLP use cases
Familiarity with healthcare data standards, terminologies, or ontologies
Experience deploying ML/NLP solutions in regulated or production healthcare environments
Knowledge of distributed systems and cloud-native data architectures
Experience with additional data stores, data warehouses, or NoSQL technologies
Strong technical documentation and stakeholder communication skills
Experience working in agile or cross-functional product development teams

Responsibilities

Design, develop, and maintain Python‑based applications, data pipelines, and AI/ML solutions.
Build, train, evaluate, and deploy machine learning and data science models for production use.
Develop and integrate Generative AI solutions using AWS Bedrock, including foundation model selection, prompt engineering, and inference orchestration.
Design and manage AWS cloud infrastructure using Terraform following IaC best practices.
Build scalable AI/ML and GenAI deployment architectures using AWS services.
Develop and optimize data ingestion, processing, and analytics pipelines for structured and unstructured data.
Collaborate with cross‑functional teams to translate business and analytical requirements into technical solutions.
Implement CI/CD pipelines, monitoring, logging, and performance optimization.
Ensure security, compliance, governance, and cost optimization of cloud and AI workloads.
Mentor junior engineers, conduct code reviews, and contribute to architectural decisions.
Create and maintain technical documentation, solution designs, and operational runbooks.