AI Engineer

Jobvite, Inc.; Chennai District; On-site; INR 4,000,000 - 7,000,000; Full time

Job summary

Jobvite, Inc. in Chennai, India, seeks a Senior AI Engineer to enhance an enterprise cloud data platform with AI, GenAI, and automation. You will own data pipelines, LLM integration, and RAG implementations while aligning with enterprise security and governance.

The role demands 6+ years in data engineering and at least 1 year in AI/GenAI, with hands-on expertise across Python, Spark, and cloud ecosystems (AWS/Azure/GCP). Expect collaboration with architecture, security, and product teams.

Qualifications

• 6+ years in Data Engineering / Cloud Data Platforms.
• Minimum 1+ year hands-on AI/Generative AI experience.
• Proficient in Python, SQL, and modern cloud data stacks.

Responsibilities

• Design and develop AI/Generative AI capabilities on the data platform.
• Build LLM-based applications and retrieval augmented generation solutions.
• Develop prompts, context management, grounding, and retrieval strategies.
• Implement AI agents and vector embeddings with secure APIs.
• Architect scalable data ingestion, processing, and APIs for AI use cases.
• Ensure observability, monitoring, and responsible AI controls.

Skills

Python

SQL

ETL/ELT

Data modeling

REST APIs

Apache Spark

AWS

Azure

GCP

LLMs

Generative AI

RAG

Embeddings

Vector databases

LLM APIs

Docker

Kubernetes

LangChain

CI/CD

Terraform

Security/IAM

Education

Bachelor's in Computer Science

Master's in a related field

Data Science

Artificial Intelligence

Tools

Databricks

Snowflake

Airflow

Weaviate

Milvus

Job 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

Bachelor's or Master's degree in:

• Computer Science
• Information Technology
• Data Science
• Artificial Intelligence
• Engineering

or a related technical discipline.