Data Science/MLOps Engineer

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.

Location: -

• Chicago, IL

Educational Qualifications: -

• Engineering Degree BE/ME/BTech/MTech/BSc/MSc.
• Technical certification in multiple technologies is desirable.

Skills: -

Mandatory skills

• 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

Good-to-Have Skills

• 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