AI/ML Engineer

Zoho; Pune District; On-site; INR 1,200,000 - 2,400,000; Full time

Job summary

Vrinda International seeks an AI/ML Engineer to design, develop, and deploy advanced ML models. You will build scalable pipelines, containerize applications, and develop RESTful APIs for microservices while leveraging vector search tools and generative AI frameworks.

The role emphasizes cloud deployments, MLOps practices, and ongoing model monitoring to ensure reliable, high-impact technical outcomes.

Qualifications

• 4–6 years in data engineering or ML roles.
• Proficient with Pandas and NumPy; Spark & SQL for big data processing.
• Containerization & orchestration: Docker, Kubernetes; Git & CI/CD.
• REST APIs design for microservices.
• Vector databases and similarity search (Pinecone/FAISS/Weaviate).
• Cloud deployments (Azure/AWS/GCP) and MLOps (MLflow/Kubeflow).

Responsibilities

• Architect and implement scalable ML pipelines using Pandas, NumPy, Spark and SQL.
• Containerize apps and manage Docker/Kubernetes with Git and CI/CD.
• Develop and integrate RESTful APIs to support microservices.
• Build and optimize vector search systems (Pinecone, Weaviase, FAISS).
• Establish model monitoring and automate retraining for reliability.
• Leverage generative AI frameworks (Hugging Face, LangChain, LLMs, RAG).
• Deploy models on cloud platforms (Azure, AWS, GCP).
• Implement MLOps using MLflow and Kubeflow.

Skills

Data engineering

Machine learning

Microservices

REST APIs

Cloud platforms

DevOps

Python (Pandas/NumPy)

Vector databases

Tools

Docker

Kubernetes

Git

CI/CD

Job description

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Job Description

Summary

We are seeking a skilled AI/ML Engineer to join our IT Services team. In this pivotal role, the successful candidate will design, develop, and deploy advanced machine learning models and artificial intelligence solutions. This position is essential for driving innovation and delivering high-impact technical outcomes that leverage data science to solve complex business challenges.

Responsibilities

• Architect and implement scalable machine learning pipelines using tools like Pandas, NumPy, Spark, and SQL.
  
• Containerize applications and manage orchestration environments with Docker and Kubernetes while maintaining version control via Git and CI/CD workflows.
  
• Develop and integrate RESTful APIs to support microservices architectures.
  
• Build and optimize vector search systems utilizing platforms such as Pinecone, Weaviase, and FAISS.
  
• Establish robust model monitoring protocols and automate the retraining processes to ensure system reliability.
  
• Leverage generative AI frameworks including Hugging Face, LangChain, LLMs, and RAG architectures.
  
• Deploy and manage models on cloud platforms including Azure ML, Azure OpenAI, AWS SageMaker, and GCP Vertex AI.
  
• Implement MLOps best practices using MLflow and Kubeflow to streamline the machine learning lifecycle.
  

Requirements

Requirements:

• Possess 4 to 6 years of professional experience in data engineering or machine learning roles.
  
• Demonstrate proficiency with data manipulation libraries such as Pandas and NumPy, alongside big data processing with Spark and SQL.
  
• Have hands-on experience with containerization, orchestration, and DevOps tools like Docker, Kubernetes, Git, and CI/CD.
  
• Show expertise in designing REST APIs and working within microservices ecosystems.
  
• Understand vector database technologies and similarity search mechanisms using Pinecone, Weaviase, or FAISS.
  
• Be capable of setting up automated model monitoring and retraining workflows.
  
• Possess strong knowledge of generative AI tools, specifically Hugging Face, LangChain, Large Language Models, and Retrieval-Augmented Generation.
  
• Have practical experience deploying models on major cloud providers, including Azure, AWS, or GCP.
  
• Be familiar with MLOps frameworks such as MLflow and Kubeflow.