ML Engineer

Ankercloud; Bengaluru Urban; On-site; INR 900,000 - 1,400,000; Full time

Job summary

Ankercloud is a global technology consulting and implementation partner focused on cloud, data, AI/ML, and security. The Machine Learning Engineer will build and deploy AI/ML models for customer use cases across cloud-native environments, collaborating with senior engineers and data teams to deliver scalable AI solutions.

The candidate should have 2–5 years of hands-on ML engineering experience, strong Python skills, and familiarity with ML frameworks, NLP/vision use cases, and MLOps practices.

Qualifications

• 2–5 years of hands-on experience in machine learning engineering or applied AI development.
• Strong proficiency in Python and ML/DL frameworks (PyTorch, TensorFlow, Scikit-learn).
• Experience building and deploying ML solutions in cloud environments (AWS or Google Cloud).

Responsibilities

• Build and deploy AI/ML models for real-world business problems across NLP, OCR, computer vision, forecasting, and Generative AI.
• Develop and deploy ML solutions in cloud-native environments (AWS/GCP).
• Collaborate with data engineers, cloud engineers, architects, and stakeholders on project delivery.
• Develop APIs and ML services for customer applications and documentation.

Skills

Python

PyTorch

TensorFlow

Scikit-learn

NLP

Computer Vision

MLOps

Cloud (AWS)

Cloud (GCP)

SageMaker

Vertex AI

Prompt engineering

Embeddings

LangChain

Vector databases

RAG

Kubernetes

Docker

Tools

Docker

Kubernetes

MLflow

Job description

Bangalore North, India

Ankercloud is a cloud consulting company that specializes in helping businesses maximize their use of public cloud platforms like AWS and Google Cloud. Our services cover a wide range of cloud solutions, including cloud operations (CloudOps), cost optimization, security, and compliance. We offer AI/ML solutions, with a focus on generative AI, predictive maintenance, and demand forecasting. Additionally, Ankercloud supports the transformation of traditional software into SaaS-based solutions, making it easier for companies to scale and innovate using cloud infrastructure.

We are scaled up from a Small to Medium phase business, We are currently expanding across several locations Globally and one of our recent launches is in the USA.

We cater to small, medium, and large enterprise businesses. Some of our clients include those in manufacturing, fin-tech, retail, healthcare and ed-tech.

We specialize in offering AWS solutions in Big data, Cloud and ML and Artificial intelligence. We have partnered with AWS, Google Cloud and Microsoft Azure.

Job Description

Machine Learning Engineer

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

About Ankercloud

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.

Role Overview

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.

What You Will Do

Build AI/ML 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).

Deployment & MLOps

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.

Who You Are

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.

Nice to Have

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.

What Should Excite You

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.