AI - Engineer

Role Overview

We are looking for an AI Engineer with a strong focus on Large Language Models (LLMs) and Generative AI to design, build, and deploy intelligent, LLM-powered systems. You'll work on prompt engineering, RAG pipelines, agentic workflows, and fine-tuning, while building robust backend services in Java and Python — with React front-end knowledge as a plus to help ship end-to-end AI features.

Responsibilities

• Design, build, and deploy LLM-powered applications — chatbots, copilots, agents, RAG systems, and summarization/extraction pipelines
• Develop and optimize prompt engineering strategies (few-shot, chain-of-thought, structured output, function/tool calling) for production use cases
• Build and maintain Retrieval-Augmented Generation (RAG) pipelines — embeddings, chunking strategies, vector databases (Pinecone, Weaviate, FAISS, pgvector)
• Fine-tune and evaluate LLMs (open-source and closed-source) using techniques like LoRA/QLoRA, instruction tuning, and RLHF where applicable
• Design and implement agentic workflows using frameworks like LangChain, LangGraph, LlamaIndex, or custom orchestration
• Build backend services and APIs in Java and/or Python to serve LLM applications at scale
• Implement evaluation frameworks for LLM outputs — accuracy, hallucination rate, latency, cost, and safety metrics
• Manage prompt versioning, model routing, and caching for cost/performance optimization across multiple LLM providers (OpenAI, Anthropic, open-source models)
• Collaborate with front-end developers (or contribute directly in React) to surface LLM features in user-facing products
• Monitor deployed LLM systems for drift, degradation, and safety/guardrail violations; iterate on mitigation strategies
• Implement guardrails, content filtering, and responsible AI practices (bias mitigation, prompt injection defense, data privacy)
• Stay current with the fast-moving LLM/GenAI landscape (new models, techniques, tooling) and assess applicability to the business

Requirements

• Bachelor's or Master's degree in Computer Science, Machine Learning, or related field (or equivalent practical experience)
• Strong programming skills in Java and Python
• Hands-on experience with LLM APIs (OpenAI, Anthropic Claude, Gemini, etc.) and open-source LLMs (Llama, Mistral, etc.)
• Practical experience with prompt engineering and RAG architecture
• Experience with vector databases and embedding models
• Familiarity with LLM orchestration frameworks: LangChain, LangGraph, LlamaIndex, Semantic Kernel, or similar
• Solid understanding of transformer architecture and how LLMs work under the hood
• Experience building and consuming REST/gRPC APIs for AI-serving backends
• Experience with cloud platforms (AWS, GCP, Azure) and containerization (Docker, Kubernetes)

Skills

• Python
• Java
• LangChain
• LLMs
• React