Job Description:

As a GenAI Engineer / Sr. GenAI Engineer, you will lead the design, development, and deployment of production-grade AI solutions. You aren't just building prototypes; you are architecting scalable RAG systems, fine-tuning models for specific domain expertise, and ensuring that GenAI capabilities integrate seamlessly into enterprise-level applications.

Key Responsibilities:

LLM Orchestration:

- Design and deploy sophisticated LLM-based applications using frameworks such as LangChain, LlamaIndex, or Semantic Kernel.

Model Optimization:

- Lead the fine-tuning of open-source and proprietary models to improve performance, latency, and cost-efficiency.

Advanced RAG Systems:

- Architect and optimize Retrieval-Augmented Generation (RAG) pipelines utilizing vector databases like Pinecone, FAISS, Weaviate, or Azure AI Search.

Scalable Deployment:

- Containerize services using Docker and deploy via FastAPI and Azure Functions to ensure high availability and low latency.

Productization:

- Build and scale enterprise-grade chatbots, copilots, and automated content generation tools using OpenAI/Azure OpenAI and Hugging Face.

Prompt Engineering:

- Implement and manage advanced prompt optimization and versioning workflows to enhance model accuracy.

Monitoring & Evaluation:

- Establish robust evaluation frameworks (e.g., RAGAS, TruLens) to track model performance, hallucination rates, and drift in production.

Operationalization:

- Collaborate with cross-functional teams (Product, DevOps, Data) to identify high-impact use cases and move them from POC to production.

Best Practices:

- Set the standard for GenAI engineering, including model selection criteria, performance tracking, and ethical AI safeguards.

Required Skills & Qualifications:

Overall Experience:

- 3 to 6 years of professional software engineering experience.

GenAI Domain Experience:

- 2 years of dedicated, hands-on experience building GenAI, LLM, and OCR solutions in production environments.

Programming Mastery:

- Deep proficiency in Python with expertise in asynchronous programming, typing standards, and clean code principles.

Generative AI & LLMs:

- Hands-on experience with commercial APIs (OpenAI GPT-4o, Anthropic Claude, Azure OpenAI) and open-source models (Llama 3, Mistral), along with modern agentic frameworks.

Vector Infrastructure & Search:

- Practical experience with hybrid search (dense + sparse), re-ranking algorithms, metadata filtering, and vector index tuning.

Backend & Cloud:

- Strong background in FastAPI, Docker, Git, CI/CD pipelines, and enterprise cloud ecosystems (Azure / AWS).

Problem Solving:

- Proven track record of solving hallucination, context window limitations, and data grounding issues in production.