AI Engineer

World Vision Softek; Bengaluru; On-site; INR 900,000 - 1,500,000; Full time

Job summary

World Vision Softek is seeking an AI Engineer to design, develop, and deploy AI/ML systems that enhance business capabilities and automate processes. The role covers architecture, data engineering, and MLOps, with a focus on productionizing models at scale.

Responsibilities include building RAG architectures, integrating AI with enterprise apps, and collaborating with cross-functional teams to translate requirements into scalable technical solutions.

Qualifications

• Strong proficiency in Python (NumPy, Pandas, PyTorch, TensorFlow, Transformers).
• Hands-on experience with LLMs (OpenAI, Azure OpenAI, Anthropic, Llama, etc.).
• Expertise in ML algorithms, NLP, deep learning, and vector embeddings.
• Experience with cloud platforms (Azure/AWS/GCP) and serverless compute.
• Familiarity with MLOps tools (MLflow, Kubeflow, Azure ML, SageMaker, or Databricks).
• Experience using vector databases (Pinecone, Chroma, FAISS, Azure AI Search).
• Knowledge of containerization (Docker, Kubernetes).

Responsibilities

• Design, develop, and deploy AI/ML solutions across business functions.
• Build scalable AI services and microservices using Python, REST APIs, and cloud-native tech.
• Implement CI/CD pipelines for ML models and monitor drift for retraining.
• Integrate AI systems with enterprise apps and cloud platforms (Azure/AWS/GCP).
• Collaborate with product and domain teams; communicate AI capabilities to non-technical stakeholders.

Skills

Python

NumPy

Pandas

PyTorch

TensorFlow

Transformers

LLMs

NLP

Vector embeddings

Tools

Pinecone

FAISS

Weaviate

Azure AI Search

Docker

Kubernetes

MLflow

Kubeflow

Azure ML

SageMaker

Databricks

Job description

We are seeking a skilled AI Engineer to design, develop, and deploy AI/ML-driven solutions that enhance business capabilities, automate processes, and improve customer and employee experiences. The ideal candidate has strong foundations in machine learning, LLMs, data engineering, and cloud platforms, with the ability to productionize models at scale.

The candidate will have responsibilities across the following functions:

AI/ML Solution Development:

• Design, build, and deploy machine learning and generative AI models (LLMs, embeddings, transformers, RAG pipelines, etc. ).
• Develop scalable AI services and microservices using Python, REST APIs, and cloud-native technologies.
• Optimise models for performance, accuracy, and cost efficiency.

Data Engineering and Preparation:

• Work with structured and unstructured datasets for feature engineering, vectorisation, and model training.
• Build data pipelines for training, validation, and inference.
• Collaborate with data engineering teams on data ingestion, storage, and governance.

Model Deployment and MLOps:

• Implement CI/CD pipelines for ML models (MLOps).
• Monitor model performance and drift; implement retraining strategies.
• Manage model lifecycle management, logging, and observability.

AI Architecture and Integration:

• Integrate AI systems with enterprise applications, APIs, and cloud platforms (Azure/AWS/GCP).
• Build Retrieval-Augmented Generation (RAG) architectures leveraging vector databases like Pinecone, FAISS, Weaviate, or Azure AI Search.
• Ensure solutions align with enterprise security, compliance, and ethical AI standards.

Cross-functional Collaboration:

• Work with product, engineering, domain experts, and business teams to translate requirements into technical solutions.
• Communicate AI capabilities and limitations to non-technical stakeholders.
• Conduct POCs, demos, and conceptual solutioning.

Requirements:

• Strong proficiency in Python (NumPy, Pandas, PyTorch, TensorFlow, Transformers).
• Hands-on experience with LLMs (OpenAI, Azure OpenAI, Anthropic, Llama, etc. ).
• Expertise in ML algorithms, NLP, deep learning, and vector embeddings.
• Experience with cloud platforms (Azure/AWS/GCP) and serverless compute.
• Familiarity with MLOps tools (MLflow, Kubeflow, Azure ML, SageMaker, or Databricks).
• Experience using vector databases (Pinecone, Chroma, FAISS, Azure AI Search).
• Knowledge of containerization (Docker, Kubernetes).