AI Engineer

Description

Role Overview

We are looking for an experienced Senior AI Engineer - Cloud Data Platform to help enhance our enterprise cloud data platform with AI, Generative AI, and intelligent automation capabilities.

The ideal candidate will bring a strong foundation in data engineering and modern cloud data platforms , combined with hands-on experience building and productionizing AI/ML and Generative AI solutions .

This role requires an engineer who can work across data pipelines, cloud services, APIs, LLMs, vector databases, and enterprise applications to build scalable AI capabilities on top of an established data platform.

Experience

Overall Experience: 6+ years in Data Engineering / Cloud Data Platforms

Relevant AI Experience: Minimum 1+ year of hands-on experience in AI, Generative AI, LLM-based applications, or ML engineering

Key Responsibilities AI Generative AI Engineering

Design and develop AI and Generative AI capabilities integrated with the existing cloud data platform.

Build enterprise applications using Large Language Models (LLMs) and foundation models.

Develop Retrieval-Augmented Generation (RAG) solutions using enterprise structured and unstructured data.

Design prompting, context management, grounding, and retrieval strategies for enterprise AI applications.

Build AI agents and agentic workflows for data discovery, analytics, operational automation, and knowledge retrieval.

Implement embeddings, semantic search, vector indexing, and vector database solutions.

Integrate enterprise data with LLM platforms and AI services through secure APIs.

Implement mechanisms for evaluating AI responses for accuracy, relevance, hallucination, and overall quality.

Develop appropriate guardrails, observability, monitoring, and responsible-AI controls for production AI applications.

Cloud Data Platform Engineering

Enhance and extend existing enterprise cloud data platform capabilities.

Design and develop scalable data ingestion, transformation, and processing pipelines.

Work with structured, semi-structured, and unstructured datasets.

Build reusable data services and APIs that can be consumed by AI applications.

Optimize data pipelines and storage for performance, scalability, reliability, and cost.

Support data quality, metadata management, lineage, governance, and security requirements.

Work with batch and real-time/streaming data processing patterns.

Ensure AI solutions integrate effectively with existing data architecture and enterprise security standards.

Solution Engineering

Translate business requirements and use cases into scalable AI/data engineering solutions.

Develop reusable frameworks and components for AI-enabled data platform capabilities.

Conduct technical POCs and rapidly evaluate new AI technologies and frameworks.

Productionize successful prototypes following enterprise engineering standards.

Collaborate with Data Architects, Data Engineers, Cloud Engineers, Data Scientists, Product Owners, and business stakeholders.

Participate in architecture/design discussions, code reviews, troubleshooting, and performance optimization.

Required Technical Skills Data Engineering

Strong hands-on experience with:

Python

SQL

Data engineering and ETL/ELT frameworks

Data modeling and data processing

REST APIs and microservices

Distributed data processing technologies such as Apache Spark

Modern cloud data platforms

Experience with one or more cloud ecosystems:

AWS

Microsoft Azure

Google Cloud Platform (GCP)

Experience with modern data platforms/technologies such as:

Databricks

Snowflake

Cloud-native data lakes/lakehouses

Delta Lake / Iceberg or similar technologies

Airflow or equivalent orchestration frameworks

AI / Generative AI

Hands-on experience with:

Large Language Models (LLMs)

Generative AI application development

Prompt engineering

Retrieval-Augmented Generation (RAG)

Embeddings and semantic search

Vector databases

LLM APIs and model integration

AI agents / agentic workflows

LLM evaluation and monitoring

Experience with frameworks/platforms such as:

OpenAI / Azure OpenAI

Anthropic Claude

Google Gemini

Hugging Face

LangChain

LangGraph

LlamaIndex

Semantic Kernel or similar AI orchestration frameworks

Experience with vector technologies such as:

Pinecone

Weaviate

Milvus

FAISS

pgvector

Azure AI Search

OpenSearch or equivalent

Preferred Skills

Experience building enterprise-grade GenAI applications rather than only prototypes or demos.

Experience implementing RAG over enterprise data sources.

Understanding of AI agents, tool calling, MCP, and multi-agent architectures .

Experience integrating AI applications with databases, APIs, enterprise applications, and document repositories.

Knowledge of MLOps / LLMOps concepts.

Experience with Docker and Kubernetes.

CI/CD and DevOps experience.

Experience with infrastructure-as-code tools such as Terraform.

Understanding of cloud security, IAM, encryption, secrets management, and data privacy.

Knowledge of data governance and responsible AI principles.

Experience implementing observability and cost monitoring for AI applications.

Education

Bachelors or Masters degree in:

Computer Science

Information Technology

Data Science

Artificial Intelligence

Engineering

or a related technical discipline.

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.