AI Architect

Job Title: AI Architect

Location: New York, NY or Jersey City, NJ (Onsite)

Job Type: Full time

The AI Architect is responsible for designing, developing, and implementing large-scale AI/ML solutions that align with enterprise goals. This role requires deep technical expertise in machine learning, generative AI, data engineering, cloud platforms, and solution architecture. The AI Architect works closely with stakeholders, engineering teams, data scientists, and leadership to build scalable, secure, and high-performing AI systems that drive business value.

Key Responsibilities

1. AI Strategy & Architecture Design

Define the enterprise AI architecture, frameworks, and roadmap aligned with business strategy.

Architect end-to-end AI/ML systems, including data pipelines, model training, deployment, monitoring, and governance.

Evaluate and select AI platforms, model types, and cloud-native technologies.

2. Solution Design & Technical Leadership

Design scalable architectures for predictive analytics, NLP, computer vision, generative AI, and LLM-based applications.

Lead PoCs, prototypes, and MVPs to validate feasibility and performance of AI solutions.

Partner with engineering teams to ensure robust and secure deployment of AI pipelines.

3. Data Engineering & MLOps

Oversee data ingestion, transformation, feature engineering, and data quality processes.

Implement MLOps practices: CI/CD, automated model training, testing, deployment, and monitoring.

Work with tools like MLflow, Kubeflow, Airflow, SageMaker, Vertex AI, Databricks, or Azure ML.

4. AI Governance & Compliance

Establish model governance, versioning, drift detection, explainability (XAI), and ethical AI guidelines.

Ensure compliance with security, privacy, and regulatory standards (GDPR, ISO, SOC, etc.).

Define risk assessment and model evaluation frameworks.

5. Stakeholder Collaboration & Consultation

Work with Product, Business, and Engineering teams to identify AI opportunities.

Translate business use cases into technical AI solutions and architectural blueprints.

Communicate complex AI concepts to non-technical stakeholders.

6. Performance Optimization & Scalability

Optimize model performance, inference latency, cost efficiency, and system reliability.

Implement distributed model training and large-scale data processing pipelines.

Ensure architectures support high-volume, real-time, or near-real-time AI workloads.

7. Continuous Innovation

Stay updated on latest AI advancements: foundation models, LLMs, agents, vector databases, and multimodal AI.

Drive adoption of generative AI frameworks (LangChain, LlamaIndex, RAG, fine-tuning).

Provide technical mentorship to data scientists and ML engineers.

Required Skills & Qualifications

* Bachelor’s or Master’s in Computer Science, AI/ML, Data Science, or related field.

* 7 plus years of experience in ML/AI engineering, ML architecture, or data engineering.

* Strong knowledge of ML algorithms, deep learning, NLP, LLMs, reinforcement learning, and generative AI.

* Proficiency in Python, SQL, and frameworks like TensorFlow, PyTorch, Scikit-Learn, Hugging Face.

* Expertise in cloud platforms: AWS, Azure, or GCP.

* Hands-on experience with MLOps tools and pipelines.

* Ability to design secure, scalable, and distributed AI systems.

* Excellent communication and leadership skills.