Key skills
PythonAIArtificial IntelligenceMLGenerative AIGenAILLMRAGLangChainPyTorchTensorFlowscikit-learnMLOpsData EngineeringAnalyticsSparkKafkaAirflowAWSAzureGoogle CloudKubernetesDockerCI/CDLeadership
About this role
Job Title: AI Architect
Location: New York City, NY (3 days/week onsite Hybrid)
Long Term Contract
Job Summary
seeking a highly skilled AI Architect to lead the design and implementation of enterprise-grade artificial intelligence solutions. This role will focus on building scalable AI/ML systems, integrating advanced analytics, and driving innovation across business units. The ideal candidate will have deep expertise in AI architecture, cloud platforms, and modern data ecosystems.
Key Responsibilities
Design and architect scalable AI/ML solutions aligned with business objectives
Lead end-to-end AI solution lifecycle: data ingestion, model development, deployment, and monitoring
Develop and implement LLM-based and GenAI solutions for enterprise use cases
Collaborate with data engineers, data scientists, and business stakeholders
Define AI architecture standards, best practices, and governance frameworks
Integrate AI models with enterprise systems via APIs and microservices
Ensure performance, scalability, and security of AI systems
Evaluate and recommend new AI tools, frameworks, and technologies
Provide technical leadership and mentorship to engineering teams
Required Skills & Qualifications
10+ years of experience in software engineering, with strong focus on AI/ML architecture
Expertise in Python, and AI/ML frameworks like TensorFlow, PyTorch, or Scikit-learn
Hands-on experience with Generative AI, LLMs, LangChain, or similar frameworks
Strong knowledge of cloud platforms (AWS, Azure, or Google Cloud Platform)
Experience with data engineering tools (Spark, Kafka, Airflow, etc.)
Solid understanding of REST APIs, microservices architecture, and system design
Experience in MLOps (model deployment, CI/CD, monitoring)
Strong problem-solving and analytical skills
Preferred Qualifications
Experience in financial services or investment banking domain
Knowledge of risk, compliance, or trading systems
Familiarity with vector databases, embeddings, and RAG architectures
Exposure to Kubernetes, Docker, and containerized deployments