LLM Engineer

We are seeking a skilled and experienced LLM Engineer with 3-5 years of relevant experience in generative AI, large language models (LLMs), and AI application development. The ideal candidate will possess strong Python programming skills, a solid understanding of LLM architecture, and hands-on experience designing, developing, and deploying scalable AI-powered applications. We are looking for an engineer with strong technical fundamentals, practical problem-solving abilities, and the capability to translate complex business requirements into reliable, production-ready AI solutions.

Responsibilities:

• Design, develop, and deploy LLM-powered applications using commercial and open-source language models.
• Build and optimize end-to-end Retrieval-Augmented Generation (RAG) pipelines.
• Develop intelligent AI agents and multi-step workflows using modern AI frameworks.
• Implement prompt engineering, structured output generation, function calling, and tool integration.
• Design scalable backend services and APIs using Python and FastAPI.
• Optimize LLM applications for accuracy, latency, cost, and performance.
• Implement evaluation frameworks to assess response quality, retrieval performance, and hallucination rates.
• Integrate vector databases, semantic search, and knowledge retrieval systems.
• Implement appropriate guardrails, monitoring, logging, and error-handling mechanisms.
• Collaborate with cross-functional teams to deliver robust and scalable AI solutions.

Requirements:

• Strong analytical and problem-solving capabilities.
• Hands-on experience building and deploying production-grade LLM applications.
• The ability to explain technical architecture, implementation decisions, and engineering trade-offs.
• Strong coding skills and practical software engineering knowledge.
• The ability to independently troubleshoot complex technical challenges.
• Excellent technical communication and collaboration skills.

Programming and Backend Development:

• Strong proficiency in Python and software engineering fundamentals.
• Experience with FastAPI, REST APIs, asynchronous programming, and API integrations.
• Strong understanding of data structures, algorithms, object-oriented programming, and clean coding practices.

Large Language Models and Generative AI:

• Strong understanding of Transformer architecture, attention mechanisms, tokenization, and embeddings.
• Hands-on experience with LLMs such as GPT, Claude, Llama, or Mistral.
• Experience with prompt engineering, few-shot learning, structured outputs, and function calling.
• Understanding of context windows, inference parameters, and LLM limitations.

Retrieval-Augmented Generation (RAG):

• Hands-on experience developing end-to-end RAG applications.
• Experience with document ingestion, preprocessing, chunking, and embedding generation.
• Familiarity with vector databases such as FAISS, Pinecone, Qdrant, Chroma, or Weaviate.
• Understanding of semantic search, hybrid search, metadata filtering, and reranking.
• Ability to evaluate and improve retrieval quality and answer accuracy.

AI Frameworks and Agentic AI:

• Experience with LangChain, LangGraph, LlamaIndex, or equivalent frameworks.
• Understanding of AI agent architecture, tool calling, workflow orchestration, and state management.
• Experience designing reliable multi-step AI workflows with appropriate error handling and validation.

Evaluation and Optimization:

• Experience evaluating LLM outputs and improving application quality.
• Understanding of retrieval and response evaluation metrics.
• Familiarity with hallucination mitigation, guardrails, and output validation.
• Experience optimizing inference latency, token usage, and application cost.

Preferred Qualifications:

• Experience with fine-tuning techniques such as LoRA, QLoRA, SFT, or PEFT.
• Familiarity with Hugging Face Transformers and open-source model deployment.
• Knowledge of model quantization and inference optimization.
• Experience with Docker, Kubernetes, and cloud platforms such as AWS, Azure, or GCP.
• Familiarity with MCP (Model Context Protocol).
• Experience with LLM observability and evaluation tools.

Skills

AI Agents, Embeddings, Evaluation, FastAPI, Generative AI, LLM, LangChain, LangGraph, Large Language Model, LlamaIndex, Python, RAG, REST API, Reranking, Retrieval Augmented Generation, Semantic Search, Tool Calling, Vector Databases