AI Engineer

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).