Job Title: AI Engineer Location: Edison, NJ (Hybrid/On-site) Full-time About the Role: We are looking for a highly skilled AI Engineer with 5 7 years of experience in building, deploying, and optimizing machine learning and generative AI solutions. The ideal candidate has strong expertise in Python, modern ML frameworks, and hands-on experience with Large Language Models (LLMs), RAG pipelines, and LangChain. You will work closely with data scientists, software engineers, and business teams to build scalable AI-driven applications and automation solutions.
Key Responsibilities:
- Design, develop, and deploy AI/ML models, including deep learning and NLP-based systems.
- Build and optimize LLM-powered applications, including custom model fine-tuning, prompt engineering, and evaluation.
- Develop RAG (Retrieval-Augmented Generation) pipelines using vector databases and retrieval frameworks.
- Build scalable LangChain-based applications and agents to orchestrate LLM workflows.
- Implement and maintain end-to-end ML pipelines supporting training, inference, and continuous model improvement.
- Develop clean, scalable Python code for AI/ML features, automation, and backend logic.
- Integrate LLMs with enterprise data sources, APIs, and cloud platforms.
- Evaluate, benchmark, and optimize model performance, latency, and reliability.
- Collaborate with product, engineering, and data teams to design AI-driven features and system architectures.
- Ensure security, compliance, and responsible use of AI in production systems.
Required Qualifications: 5 7 years of experience in AI Engineering, ML Engineering, or similar roles. Strong proficiency in Python and related ML/NLP libraries (TensorFlow, PyTorch, Scikit-learn, Transformers, etc.). Hands-on experience working with Large Language Models (OpenAI, Anthropic, Llama, Mistral, etc.). Experience building RAG systems using vector databases (FAISS, Pinecone, Weaviate, Milvus, Chroma, etc.). Practical experience with LangChain, LlamaIndex, or other LLM orchestration frameworks. Strong understanding of prompt engineering, embeddings, tokenization, and LLM evaluation. Experience deploying AI models into production using cloud platforms (Azure, AWS, Google Cloud Platform). Familiarity with modern MLOps practices-model versioning, CI/CD, monitoring, and drift detection. Strong understanding of APIs, microservices, and integration patterns. Excellent problem-solving, debugging, and communication skills.
Preferred Skills: Experience with Docker, Kubernetes, or serverless deployments. Familiarity with Databricks, Spark, or distributed compute environments. Experience with tools like Hugging Face Hub, OpenAI Assistants API, or LangGraph. Background in vector search optimization, embedding models, and document chunking strategies. Knowledge of security, compliance, and responsible AI practices.
Education: Bachelor's or Master's degree in Computer Science, Data Science, AI/ML, Engineering, or a related field (or equivalent practical experience).
For applications and inquiries, contact: [email protected]