Job Description:

We are looking for an Enterprise AI Engineer with strong hands-on experience in designing and implementing Gen-AI solutions for enterprise use cases.

The ideal candidate should have deep understanding of AI and Gen-AI concepts, including LLMs, prompt engineering, RAG, embeddings, vector databases, agents, model evaluation, API integrations, and production deployment practices.

The candidate should have prior experience working on Gen-AI projects that have moved beyond proof of concept and have been deployed in production.

They should be comfortable working with cross-functional enterprise IT teams, understanding operational challenges, and building AI-enabled solutions that automate support, analysis, documentation, and execution activities.

The candidate should bring strong problem-solving ability, engineering discipline, and a practical mindset to convert AI opportunities into reliable, secure, and scalable enterprise solutions.

Years of Experience:

8 - 12 years.

Role:

The Enterprise AI Engineer will be responsible for working with IT application teams to discover, validate, and implement practical Gen-AI use cases that can move from proof of concept to production.

The person will analyze existing IT processes, SOPs, ticket patterns, knowledge documents, system logs, and operational workflows to identify automation opportunities.

They will design AI solution architectures using LLMs, RAG pipelines, agentic workflows, APIs, and integration layers.

They will build, test, deploy, and support Gen-AI solutions in enterprise environments while ensuring security, governance, auditability, scalability, and compliance.

The engineer will also collaborate with platform, infrastructure, security, data, and application teams to productionize AI solutions.

The role requires strong hands-on implementation capability, the ability to translate business problems into AI solutions, and prior experience delivering Gen-AI projects that have successfully gone live in production.

Core Engineering Background:

- 5+ years of experience in enterprise software development, backend engineering, or systems integration, with a heavy emphasis on Python, node.js, or cloud-native microservices.

Generative AI Core Competency:

- Deep, practical experience building applications with Large Language Models (LLMs), designing Retrieval-Augmented Generation (RAG) pipelines, and working with vector databases (e.g., pgvector, Pinecone, or custom S3-backed vector files).

Integration Expertise:

- Demonstrated mastery in API development (REST, gRPC) and connecting conversational layers to complex backend transaction systems (specifically SAP via web services and enterprise ticketing tools like JIRA/BMC Helix).

Protocol & Systems Familiarity:

- Exposure to modern AI agent communication frameworks, multi-agent swarms, or open standards like the Model Context Protocol (MCP).

Security Awareness:

- Solid understanding of the OWASP Top 10 for LLMs and standard data-masking/encryption approaches.