Principal AI/ML Architect: Enterprise GenAI Lead

Slalom

Hartford (CT)

On-site

USD 157,000 - 196,000

Full time

2 days ago

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Job summary

Slalom is seeking an AI/ML Architect to design production-grade AI systems across AWS, Azure, and Google Cloud, including Bedrock, Vertex AI, and enterprise data platforms. You’ll lead cross-functional teams to deliver secure, scalable, and governance-ready architectures for enterprise clients.

Based in or willing to be onsite in clients or Slalom offices, you’ll mentor delivery teams, contribute to business development, and coach junior consultants while shaping AI roadmaps and adoption plans

Qualifications

6+ years of experience implementing ML/AI solutions in production.
3+ years of professional consulting or IT services.
Hands-on experience designing end-to-end AI systems.
Deep understanding of GenAI patterns and RAG.
Experience with agentic AI architecture patterns.
Proficiency with modern AI engineering frameworks.
Strong Python programming and modern software practices.
Experience with cloud AI/ML platforms (Bedrock, SageMaker, Vertex AI).
Experience with Databricks, Snowflake, Spark, Kafka, dbt, vector databases.
MLOps, CI/CD, containerization, Kubernetes, cost optimization.
Ability to communicate complex AI concepts to stakeholders.
Experience managing delivery teams and roadmaps.

Responsibilities

Architect and deliver enterprise-scale AI systems spanning data products, retrieval pipelines, model orchestration, agentic workflows, evaluation, deployment, monitoring, optimization, and lifecycle management.
Design secure, scalable, cloud-native and hybrid architectures across AWS, Azure, and Google Cloud.
Lead applied AI solution design across generative AI, agentic AI, multimodal AI, and knowledge assistants.
Enable production GenAI adoption including tool calling, structured outputs, and memory patterns.
Define AI evaluation, observability, and reliability patterns including automated evals and latency monitoring.
Champion Responsible AI and AI security practices including governance and privacy.
Evaluate emerging models and platforms, giving practical deployment recommendations.
Lead and mentor cross-functional delivery teams to ensure on-time, high-quality outcomes.
Support business development through proposals, pitches, accelerator references, and thought leadership.
Coach junior consultants to foster learning and responsible AI adoption.