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
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