Job Summary

We are seeking an experienced LLM Engineer (Large Language Model Engineer) to design, develop, deploy, and optimize enterprise-grade applications powered by Large Language Models (LLMs). You will work with foundation models, Retrieval-Augmented Generation (RAG), AI agents, vector databases, and cloud-native infrastructure to build scalable AI solutions for real-world business challenges.

The ideal candidate has hands-on experience with modern AI frameworks, transformer architectures, model evaluation, prompt engineering, distributed inference, and production AI systems. You will collaborate with AI researchers, machine learning engineers, data scientists, software engineers, and product managers to deliver innovative AI products.

Key Responsibilities

• Design, develop, and deploy production-ready applications powered by Large Language Models (LLMs).
• Build and optimize Retrieval-Augmented Generation (RAG) pipelines using vector databases and enterprise knowledge sources.
• Fine-tune and evaluate open-source and commercial foundation models for domain-specific use cases.
• Develop scalable inference APIs and microservices for AI applications.
• Implement prompt engineering, prompt optimization, and structured output techniques.
• Build autonomous AI agents and multi-agent workflows for business automation.
• Integrate LLMs with internal systems, third-party APIs, and enterprise applications.
• Develop data ingestion, embedding, indexing, and semantic search pipelines.
• Monitor model performance, latency, hallucinations, token usage, and operational costs.
• Optimize GPU utilization and inference performance for high-throughput AI workloads.
• Implement guardrails, content filtering, and AI safety controls for responsible AI deployment.
• Collaborate with DevOps and MLOps teams to automate AI model deployment and monitoring.
• Perform model evaluation using automated benchmarks and human feedback.
• Maintain technical documentation, architecture diagrams, and deployment procedures.
• Stay current with advances in Generative AI, transformer models, reasoning models, and AI agent frameworks.

Required Qualifications

• Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Machine Learning, Software Engineering, Data Science, or a related field.
• 4+ years of experience in Machine Learning, Artificial Intelligence, or Software Engineering.
• Hands-on experience building applications using Large Language Models (LLMs).
• Strong programming skills in Python.
• Experience with transformer architectures and modern NLP techniques.
• Proficiency with REST APIs and microservices architecture.
• Experience with Docker, Kubernetes, and cloud platforms.
• Strong understanding of distributed systems and scalable AI deployments.
• Excellent analytical, communication, and problem-solving skills.

Preferred Qualifications

• Experience fine-tuning open-source models such as Llama, Mistral, Qwen, or Gemma.
• Experience building RAG systems for enterprise search and knowledge management.
• Knowledge of AI agents, tool calling, and workflow orchestration.
• Experience with distributed inference frameworks such as vLLM or TensorRT-LLM.
• Familiarity with model evaluation frameworks and observability tools.
• Understanding of AI governance, privacy, and responsible AI practices.
• Experience with MLOps pipelines and continuous model deployment.

Technical Skills

Programming Languages

• Python
• SQL
• JavaScript (Basic)
• Bash

AI & Machine Learning

• Large Language Models (LLMs)
• Transformer Architecture
• Generative AI
• Natural Language Processing (NLP)
• Deep Learning
• Reinforcement Learning from Human Feedback (RLHF)
• Supervised Fine-Tuning (SFT)
• Parameter-Efficient Fine-Tuning (LoRA/QLoRA)

AI Frameworks & Libraries

• PyTorch
• TensorFlow
• Hugging Face Transformers
• Hugging Face Accelerate
• LangChain
• LlamaIndex
• DSPy
• OpenAI SDK
• Anthropic SDK

Vector Databases

• Pinecone
• Weaviate
• Milvus
• Chroma
• FAISS

AI Infrastructure

• Docker
• Kubernetes
• Ray
• vLLM
• NVIDIA TensorRT-LLM
• NVIDIA CUDA

Cloud Platforms

• Amazon Web Services (AWS)
• Microsoft Azure
• Google Cloud Platform (GCP)

Databases & Storage

• PostgreSQL
• MongoDB
• Redis
• Elasticsearch

DevOps & MLOps

• Git
• GitHub Actions
• MLflow
• Weights & Biases
• Apache Airflow
• CI/CD Pipelines

Preferred Certifications

• AWS Certified Machine Learning – Specialty
• Microsoft Certified: Azure AI Engineer Associate (AI-102)
• Google Professional Machine Learning Engineer
• NVIDIA Certified AI Infrastructure Professional
• Databricks Certified Machine Learning Professional
• TensorFlow Developer Certificate (where applicable)

Soft Skills

• Strong analytical and problem-solving abilities
• Excellent verbal and written communication
• Cross-functional collaboration
• Product-focused mindset
• Attention to detail
• Adaptability in a fast-paced environment
• Continuous learning and research orientation
• Technical leadership and mentoring

Benefits

• Competitive salary with annual performance bonus
• Equity or Restricted Stock Units (RSUs) (where applicable)
• Medical, Dental, and Vision insurance
• Flexible remote and hybrid work options
• Paid Time Off (PTO) and paid company holidays
• Professional development and certification reimbursement
• Annual learning and conference budget
• Employee wellness and mental health programs
• Paid parental leave
• Access to advanced AI infrastructure and GPU resources
• Career growth opportunities in cutting-edge AI research and engineering