ML Engineer

Location: Bengaluru
Mode: Work from office
Type: Full-time

Ankercloud is a global technology consulting and implementation partner that helps ambitious companies turn bold ideas into real products using cloud, data, AI/ML, and security. Ankercloud is a Premier Tier Partner for both AWS and Google Cloud, with teams serving customers across regions and industries.

In AI/ML, Ankercloud works on production-grade machine learning, predictive analytics, computer vision, NLP, MLOps, Generative AI, and Agentic AI, delivering solutions from MVPs and proof-of-concepts to enterprise-scale deployments.

Ankercloud is looking for a Machine Learning Engineer with 2–5 years of experience to help build and deploy AI/ML solutions for customer use cases across cloud-native environments.

This role is ideal for someone with strong fundamentals in machine learning, hands-on development experience, and exposure to deploying ML applications in production. You will work closely with senior engineers, architects, data engineers, and customers to build scalable and reliable AI solutions.

• Develop and deploy machine learning models for real-world business problems.
  

• Work on use cases across NLP, OCR, computer vision, forecasting, recommendation systems, and Generative AI and agentic AI applications.
  

• Perform data preparation, feature engineering, model training, evaluation, and optimization.
  

• Build and integrate APIs and ML services into customer applications.
  

• Assist in developing LLM-based applications using prompt engineering, embeddings, vector search, and retrieval-augmented generation (RAG).
  

  
  

• 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.