AI Engineer Enterprise GenAI

About the Role

We are looking for a hands-on AI Engineer – Enterprise GenAI to help build, integrate, deploy, and optimize production-grade AI solutions for enterprise applications.

You will work across the AI engineering lifecycle, including LLM application development, RAG systems, AI agents, model integration, evaluation, API development, deployment, and performance optimization.

The role involves close collaboration with AI architecture, product engineering, DevOps, QA, and other technical teams to develop reliable and scalable AI capabilities.

The ideal candidate combines strong Python and software engineering fundamentals with practical experience building and deploying AI/GenAI applications.

Must-Have Qualifications

• Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, Software Engineering, or a related technical field.
• 3–6 years of professional software engineering experience.
• Hands-on experience developing and deploying production AI or Generative AI applications.
• Strong professional experience with Python.
• Experience developing or integrating AI/LLM-powered applications.
• Strong understanding of REST APIs and backend application development.
• Experience working with AI models or LLM-based applications in a software engineering environment.
• Experience with RAG, embeddings, semantic search, or knowledge-retrieval systems.
• Experience with relational databases and SQL.
• Experience using Git and standard software development practices.
• Strong problem-solving and analytical skills.
• Ability to write maintainable, well-tested, and documented code.
• Strong communication and collaboration skills.
• Ability to work effectively with cross-functional engineering teams.

Preferred Qualifications

Experience with some of the following technologies and areas is highly desirable:

GenAI & Model Development

• Hugging Face Transformers
• PyTorch
• LLM application development
• Prompt engineering
• LoRA / QLoRA / PEFT
• AI model evaluation and benchmarking
• Open-source models such as Llama, Qwen, Gemma, Phi, or Mistral

RAG & Knowledge Systems

• LangChain
• LangGraph
• LlamaIndex
• Embeddings
• Semantic search
• Milvus
• Qdrant
• Weaviate
• Pinecone

AI Agents

• AI agent development
• Tool calling
• Agent frameworks
• Model Context Protocol (MCP)

AI Infrastructure

• Docker
• Kubernetes
• Linux
• GPU-based AI workloads
• vLLM
• Hugging Face TGI
• PostgreSQL
• Redis
• S3-compatible object storage
• AWS, Azure, or GCP

Advanced AI

• Vision Language Models (VLMs)
• OCR
• Document intelligence
• TensorRT-LLM
• NVIDIA Triton
• MLflow
• Ray
• Model quantization
• Distributed inference

Key ResponsibilitiesAI Application Development

• Build production-ready AI applications using LLMs, SLMs, and VLMs.
• Develop scalable AI APIs and backend services using Python and FastAPI.
• Integrate AI capabilities into enterprise applications and software products.
• Design reliable AI-powered features that meet production requirements.

Model Integration & Optimization

• Integrate open-source language and multimodal models.
• Implement model adaptation and fine-tuning workflows where required.
• Work with inference technologies such as vLLM or Hugging Face TGI.
• Evaluate model quality, latency, resource utilization, and reliability.
• Apply prompt engineering and evaluation techniques to improve application performance.

RAG & Knowledge Systems

• Build document ingestion and processing pipelines.
• Implement document chunking, embedding, indexing, and retrieval workflows.
• Develop and maintain RAG pipelines.
• Integrate vector databases and semantic search capabilities.
• Improve retrieval quality and relevance through testing and evaluation.

AI Agents & Automation

• Develop AI agents capable of supporting multi-step workflows.
• Implement tool calling and AI-driven workflow automation.
• Integrate AI agents with enterprise applications, APIs, and external tools.
• Evaluate and apply appropriate agent frameworks and emerging protocols such as MCP.

Software Engineering

• Build and maintain REST APIs and backend services.
• Write clean, maintainable, well-tested Python code.
• Participate in code reviews and technical discussions.
• Debug application and integration issues.
• Apply software engineering best practices to AI systems.
• Collaborate with engineering teams to integrate AI components into existing applications.

Infrastructure & Deployment

• Containerize AI applications using Docker.
• Support deployment and orchestration using Kubernetes.
• Work with Linux-based and GPU-enabled environments.
• Support production deployments, monitoring, troubleshooting, and performance improvements.
• Collaborate with DevOps and infrastructure teams on reliable AI deployments.

Cross-Functional Collaboration

• Work with Product, QA, Engineering, DevOps, and AI Architecture teams.
• Translate technical requirements into practical AI solutions.
• Participate in technical design discussions.
• Communicate implementation decisions, risks, and performance considerations clearly.

What We're Looking For

We are looking for an engineer who can go beyond experimentation and contribute to production AI systems.

You should be comfortable moving between:

Model → Application → API → Data → RAG → Deployment → Production

The strongest candidates will have experience taking AI capabilities from development and experimentation into reliable software applications.