Machine Learning Engineer

# Machine Learning Engineer ## Company Overview (Not specified) ## Job Summary We are seeking skilled **Machine Learning Engineers** to design, develop, and deploy advanced machine learning models focused on **Agentic AI** use cases, utilizing the **AWS ecosystem**. The role emphasizes end-to-end ML solutions, from data preprocessing and model development to deployment and ongoing monitoring, with a strong focus on **scalability**, **performance**, and **cost-efficiency** in production environments. ## Responsibilities - Design, develop, and deploy machine learning models tailored for Agentic AI applications in clinical and enterprise domains. - Work extensively with the AWS AI/ML ecosystem, including **SageMaker**, **Bedrock**, **Lambda**, **Step Functions**, **S3**, **DynamoDB**, and **Kinesis**, to build scalable solutions. - Perform data preprocessing and feature engineering on structured, unstructured, and streaming data to build high-quality training datasets. - Collaborate with Data Engineering teams to ensure robust, well-curated datasets while maintaining **PHI/PII safety** and compliance. - Implement fine-tuning of large language models (LLMs), embeddings, and retrieval-augmented generation (RAG) pipelines. - Evaluate and optimize models focusing on **accuracy**, **performance**, **scalability**, and **operational cost-effectiveness**. - Integrate ML models into production applications and expose them via **APIs** for seamless consumption. - Work closely with MLOps teams to automate workflows for model training, testing, deployment, and continuous monitoring. - Conduct experimentation, **A/B testing**, and rigorous model validation to guarantee performance and reliability. - Document experiments, data pipelines, model architectures, and best practices to ensure reproducibility and knowledge sharing. ## Qualifications - 3–6 years of hands-on experience in machine learning engineering, model development, and production deployment. - Strong proficiency in **Python**, with expertise in libraries such as **NumPy**, **Pandas**, **Scikit-learn**, **PyTorch**, and **TensorFlow**. - Solid understanding of the full ML lifecycle, including data preprocessing, model training, evaluation, deployment, and monitoring. - Experience working with AWS services for machine learning: **SageMaker**, **Lambda**, **ECS/EKS**, **Step Functions**, **Bedrock**, **S3**, and **DynamoDB**. - Practical knowledge of large language models (LLMs), natural language processing (NLP), and vector embeddings. - Proficient in developing **APIs** and deploying ML models as microservices in production environments. - Experience in orchestrating ML pipelines using tools such as **Airflow**, **Kubeflow**, or **MLflow**. - Familiarity with data versioning, experiment tracking, and model registry best practices. - Strong **SQL** and **NoSQL** database skills, including experience with vector databases like **Weaviate**, **Pinecone**, or **FAISS**. ## Preferred Skills (Not specified) ## Experience - 3–6 years of hands-on experience in machine learning engineering, model development, and production deployment. ## Environment - Remote position, working within a dynamic and supportive team environment. ## Salary (Not specified) ## Growth Opportunities (Not specified) ## Benefits (Not specified)