AI Architect

Description

The company is looking for an experienced AI architect to lead the design and delivery of new intelligent solutions across the organization. The role supports its digital transformation and centers on building secure, scalable AI platforms and capabilities.

You will oversee the full AI lifecycle, from concept and solution design through model building and testing to production rollout. You will bring deep knowledge of machine learning, generative AI, coding assistants, AI assistants, and Agile engineering. Ideal candidates have an architecture background, hands-on work with current AI platforms, and a record of shipping complex enterprise systems. You will work with technology and business teams to shape the AI ecosystem.

The company provides IT consulting, staffing, and software services. It has been headquartered in Harrison, New Jersey since 1996 and has over 20 years of experience. Its clients include systems integrators and companies in banking, telecoms, pharma and life sciences, energy, healthcare, technology, transportation, and local and federal government.

Responsibilities

• Design and guide enterprise AI and ML solutions that tackle hard business problems and power intelligent applications.
• Work with data scientists, ML and platform engineers, and software teams to embed AI into products, platforms, and workflows.
• Lead technically in Python, including ML, generative AI, and LLM frameworks such as TensorFlow, PyTorch, HuggingFace, and LangChain.
• Oversee data preparation, feature engineering, vectorization, and model building so data is ready for LLM and generative AI work.
• Track advances in AI, ML, LLMs, and agentic systems, and fold useful ones into roadmaps and designs.
• Tune AI and generative models for speed, latency, scale, observability, and cost using cloud-native and distributed compute.
• Lead technical design reviews, code reviews, and architecture assessments to keep quality high.
• Find and fix complex faults in AI systems to keep production applications reliable, accurate, and secure.
• Design company-wide capabilities built on AI coding assistants, copilots, and developer tools such as GitHub Copilot and Azure AI Studio assistants.
• Build extensions, plugins, and integrations for Visual Studio Code, IntelliJ IDEA, and Eclipse that bring AI into developer workflows.
• Apply prompt engineering, knowledge of LLM behavior, context window tuning, fine-tuning, and agentic workflows to improve results.
• Design, train, and evaluate custom generative models, including GANs, VAEs, diffusion models, and transformer architectures.
• Build pipelines for data intake, cleaning, text chunking, retrieval augmentation (RAG), and loading into vector stores.
• Connect enterprise systems to AI platforms and APIs such as OpenAI, Azure OpenAI, LangChain, and other orchestration frameworks.
• Design and maintain the LLM data layer using Azure Cosmos DB, Azure AI Search, vector stores, and embedding indexes.
• Build agentic automation with LangChain, LlamaIndex, Semantic Kernel, and AutoGen, and integrate it with enterprise systems.
• Run CI/CD pipelines with GitHub Actions, Azure DevOps, and modern testing so AI development stays automated and dependable.
• Lead or contribute to copilot initiatives for application development, customer support, operations, and productivity automation.
• Design, test, and refine high-quality prompts, prompt templates, and generative AI interaction models for company use cases.
• Partner with product, content, and data teams so prompt behavior matches business goals, brand voice, and user experience needs.
• Improve prompt structures for accuracy, relevance, safety, and context, and test chain-of-thought, retrieval-augmented, tool-augmented, and system message approaches.
• Build and maintain an enterprise prompt library with versioning, governance, reuse, and integration into automated workflows and applications.
• Help with fine-tuning and reinforcement learning by writing ground-truth prompts, evaluation prompts, and tuning datasets.
• Diagnose prompts that return poor or incorrect results and refine them so LLM behavior stays predictable across use cases.

Requirements

• Strong hands-on coding in at least one modern language such as Python, C, Java, or JavaScript, and the ability to guide engineers on best practice.
• Deep working knowledge of TensorFlow, PyTorch, and Keras, with the ability to design scalable training and inference pipelines.
• Advanced understanding of generative AI architectures such as GANs, VAEs, and transformer-based models, and how to apply them to real enterprise problems.
• Practical experience using AI coding assistants.
• Hands-on experience with agentic AI platforms such as Microsoft Copilot Agents, AutoGPT, CrewAI, Anthropic Claude, and other autonomous workflow systems.
• Strong command of cloud and MLOps platforms such as AWS SageMaker, Azure Machine Learning, and Google AI Platform, including building automated deployment, monitoring, and retraining pipelines.
• Solid experience designing solutions on AWS, Azure, or GCP, with familiarity in cloud-native AI, data, and security services.
• Proven ability to mentor and direct junior and mid-level AI and ML engineers across several projects.
• Ability to run AI, ML, and generative AI code reviews against architecture standards, performance benchmarks, and responsible AI practices.
• Strong analytical and problem-solving skills, with the ability to quickly diagnose issues in AI systems, ML pipelines, and application runtimes.
• Clear communication of complex AI concepts to both technical teams and business stakeholders.
• Experience defining enterprise AI architectures, reference models, reusable design patterns, and organization-wide architectural standards.
• Experience building AI governance frameworks covering Responsible AI guidelines, compliance controls, model guardrails, safety protocols, and risk mitigation.

Conditions

Candidate information is handled confidentially in line with EEO guidelines.