ML Engineer

Support deployment and maintenance of ML models in AWS and GCP environments.

Work with MLOps tools and workflows for model versioning, CI/CD, monitoring, and experimentation.

Use cloud AI services such as Amazon SageMaker, AWS Bedrock, Vertex AI, and related services.

Monitor model performance and support troubleshooting, optimization, and continuous improvement.

Collaborate with cross-functional teams including data engineers, cloud engineers, architects, and business stakeholders.

Participate in customer discussions, demos, and solution delivery activities.

Contribute to documentation, reusable components, and engineering best practices.

2–5 years of hands-on experience in machine learning engineering or applied AI development.

Strong proficiency in Python and experience with ML/DL frameworks such as PyTorch, TensorFlow and Scikit-learn.

Experience building and deploying ML solutions in cloud environments (AWS or Google Cloud).

Familiarity with NLP, computer vision, OCR, recommendation systems, or Generative AI & agentic AI use cases.

Basic understanding of MLOps concepts such as model deployment, monitoring, MLflow, Docker, or CI/CD workflows.

Exposure to AWS and GCP AI/ML services such as SageMaker, Bedrock, Vertex AI, or related services.

Good problem-solving and communication skills.

Ability to work collaboratively in a fast-paced, customer-focused environment.

Exposure to LangChain, vector databases, embeddings, or RAG-based applications.

Familiarity with Kubernetes, Docker, or scalable deployment architectures.

Experience working on customer-facing or consulting projects.

Understanding of Agentic AI concepts or workflow orchestration frameworks.

Opportunity to work on diverse AI/ML and Generative AI projects across industries.

Exposure to modern AWS and Google Cloud AI ecosystems.

Fast learning environment with opportunities to grow into advanced ML and AI engineering roles.

Collaborative engineering culture focused on innovation and customer impact.