AI Developer

1 week ago

Cochin, Kerala, India beBeeMachineLearning Full time ₹ 2,00,00,000 - ₹ 2,50,00,000
Job Description

We are seeking skilled Machine Learning Engineers to design, develop and deploy advanced ML models focused on Agentic AI use cases.

  • 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 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.
Required Skills & Qualifications:
  • 3+ 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.

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