Artificial Intelligence Engineer

Job Title: AI Engineer

Location: Newark, NJ, USA

Experience: 2–6 years

About Us

We are an executive search firm currently hiring on behalf of our client. Our focus is on identifying professionals whose skills and experience align with our clients' strategic hiring needs.

About the Company

Our company is a leading operations management and analytics organization that helps businesses improve growth and profitability through analytics, AI, automation, and digital transformation. We partner with global organizations across insurance, healthcare, banking, financial services, utilities, retail, travel, and logistics to solve complex business challenges using data-driven solutions. This role sits within a team that builds enterprise-grade AI capabilities for real business outcomes, combining machine learning, deep learning, cloud AI platforms, and modern software engineering practices. The focus is on taking AI solutions from concept to production and making them reliable, scalable, and useful for internal teams and clients alike. The organization values practical innovation, cross-functional collaboration, and measurable impact through AI-powered products, copilots, intelligent agents, and automated workflows.

About the Role

This role owns the design, development, and deployment of production-ready AI and Machine Learning solutions, including Generative AI applications, intelligent agents, and AI-powered products. It requires strong execution across model development, MLOps, cloud deployment, and software engineering so solutions are not only accurate but also scalable, monitored, and maintainable. The successful candidate will work across the full delivery lifecycle, from experimentation and pipeline design to APIs, microservices, and production monitoring. The role is best suited to someone who can combine hands-on engineering with practical judgment, translating complex AI possibilities into business-ready systems with clear performance, latency, and reliability outcomes.

Key Responsibilities

• Design, develop, and deploy AI and machine learning models for production use so business teams can rely on solutions that are stable, measurable, and built for scale.
• Build Generative AI applications, copilots, and intelligent agents that improve knowledge access, task automation, and user experience across business workflows.
• Develop RAG pipelines using vector databases and large language models to improve retrieval quality, response relevance, and knowledge-grounded outputs.
• Create scalable data pipelines, feature engineering workflows, and model training pipelines that support repeatable experimentation and faster delivery.
• Develop APIs and microservices using Python so AI capabilities can be integrated cleanly into enterprise applications and digital products.
• Deploy and monitor models using MLflow, Docker, and CI/CD to keep releases controlled, observable, and production-ready.
• Work with Azure ML, SageMaker, or Vertex AI to operationalize models in cloud environments with strong reliability and governance.
• Collaborate with data scientists, engineers, and business stakeholders to align AI solutions with real priorities, delivery constraints, and measurable impact.

Essential Skills & Technologies

• Strong Python programming skills with the ability to build production-grade services, automation, and AI workflows that are maintainable and efficient.
• Hands-on experience with TensorFlow, PyTorch, and Scikit-learn to develop, train, evaluate, and refine machine learning and deep learning models.
• Practical knowledge of machine learning, deep learning, statistics, and feature engineering to convert data into robust and useful predictive systems.
• Experience with MLOps practices using MLflow, Docker, and CI/CD so model deployment, monitoring, and iteration happen in a controlled and repeatable way.
• Experience with Azure ML, AWS SageMaker, or Google Vertex AI to run models on cloud AI platforms with production readiness in mind.
• Knowledge of LLMs, prompt engineering, RAG, and vector databases to support modern Generative AI use cases with grounded responses.
• Experience building REST APIs and microservices so AI capabilities can be exposed reliably to products, platforms, and internal systems.
• Familiarity with modern AI engineering tools such as LangChain, LangGraph, LlamaIndex, Pinecone, FAISS, ChromaDB, Milvus, FastAPI, Flask, Kubernetes, or Azure OpenAI Services to move solutions faster from prototype to deployment.

Additional Plus

• Exposure to LangChain, LangGraph, or LlamaIndex is a strong advantage for building more capable Generative AI workflows and orchestration layers.
• Experience with Pinecone, FAISS, ChromaDB, or Milvus is valuable for designing retrieval systems that improve answer relevance and knowledge access.
• Familiarity with Kubernetes and Azure OpenAI Services is a plus for teams operating at enterprise scale with modern cloud-native AI stacks.

What You'll Bring

• You bring 2–6 years of AI/ML engineering experience and can work confidently across model development, deployment, and production support without losing sight of business outcomes.
• You combine strong coding discipline with practical AI judgment, making trade-offs that improve reliability, scalability, and delivery speed.
• You communicate effectively with technical and non-technical stakeholders, turning AI requirements into solutions that can be understood, adopted, and trusted.
• You take ownership of quality, performance, and operational readiness, ensuring what is built can actually run well in real environments.

Why Join Us

Join us if you want to build AI solutions that move beyond experimentation and into real operational value. This role gives you the chance to work on Generative AI, intelligent agents, MLOps, and cloud AI platforms in an environment that values practical engineering and measurable outcomes. You will collaborate with smart, cross-functional teams and help shape systems that improve how business users access knowledge, automate work, and deliver services at scale. The opportunity is ideal for someone who wants meaningful ownership, modern tools, and the chance to influence how enterprise AI gets built and deployed. If you enjoy solving complex problems, working with evolving AI technologies, and turning ideas into dependable products, this is a strong fit.