Machine Learning Engineer

We are seeking skilled Machine Learning Engineers to design, develop, and deploy advanced ML 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.

Key Responsibilities:

1. Design, develop, and deploy machine learning models tailored for Agentic AI applications in clinical and enterprise domains.
2. Work extensively with the AWS AI/ML ecosystem, including SageMaker, Bedrock, Lambda, Step Functions, S3, DynamoDB, and Kinesis, to build scalable solutions.
3. Perform data preprocessing and feature engineering on structured, unstructured, and streaming data to build high-quality training datasets.
4. Collaborate with Data Engineering teams to ensure robust, well-curated datasets while maintaining PHI/PII safety and compliance.
5. Implement fine-tuning of large language models (LLMs), embeddings, and retrieval-augmented generation (RAG) pipelines.
6. Evaluate and optimize models focusing on accuracy, performance, scalability, and operational cost-effectiveness.
7. Integrate ML models into production applications and expose them via APIs for seamless consumption.
8. Work closely with MLOps teams to automate workflows for model training, testing, deployment, and continuous monitoring.
9. Conduct experimentation, A/B testing, and rigorous model validation to guarantee performance and reliability.
10. Document experiments, data pipelines, model architectures, and best practices to ensure reproducibility and knowledge sharing.

Required Skills & Qualifications:

1. 3+ years of hands-on experience in machine learning engineering, model development, and production deployment.
2. Strong proficiency in Python, with expertise in libraries such as NumPy, Pandas, Scikit-learn, PyTorch, and TensorFlow.
3. Solid understanding of the full ML lifecycle, including data preprocessing, model training, evaluation, deployment, and monitoring.
4. Experience working with AWS services for machine learning: SageMaker, Lambda, ECS/EKS, Step Functions, Bedrock, S3, and DynamoDB.
5. Practical knowledge of large language models (LLMs), natural language processing (NLP), and vector embeddings.
6. Proficient in developing APIs and deploying ML models as microservices in production environments.
7. Experience in orchestrating ML pipelines using tools such as Airflow, Kubeflow, or MLflow.
8. Familiarity with data versioning, experiment tracking, and model registry best practices.
9. Strong SQL and NoSQL database skills, including experience with vector databases like Weaviate, Pinecone, or FAISS.

Preferred Attributes:

1. Experience in healthcare or regulated domains, ensuring compliance with industry standards.
2. Excellent problem-solving skills, with a passion for experimentation, iteration, and delivering innovative AI solutions.
3. Strong communication skills, capable of collaborating with cross-functional teams.
4. Self-driven, with a focus on automating and improving ML workflows and processes.