Senior Specialist Solutions Architect
Specialist Solutions Architect who guides customers in designing and productionizing ML and AI workloads, including generative AI, agentic systems, and MLOps. Requires 5+ years of hands-on ML experience, advanced quantitative education or equivalent experience, and strong technical communication skills.
About the job
Responsibilities
- Architect production-level ML and AI workloads for customers using a unified platform, including agents, end-to-end ML pipelines, training and inference optimization, cloud-native service integrations, and MLOps.
- Serve as a trusted practitioner for enterprise generative AI solutions, including retrieval-augmented generation (RAG) architectures, agentic systems, tool-calling agents, multi-agent orchestration, guardrails, natural-language querying of structured data, AI evaluation and observability, and monitoring systems.
- Build, scale, and optimize customer AI workloads and apply best-in-class MLOps to productionize them across varied domains.
- Provide advanced technical support to Solution Architects during technical sales, covering feature engineering, training, tracking, serving, and model monitoring.
- Participate in the broader ML subject-matter-expert community.
- Collaborate with product and engineering teams to represent customer needs, define priorities, influence the product roadmap, and drive adoption of AI offerings.
Requirements
- 5+ years of hands-on industry machine learning experience in at least one of the following areas:
- ML engineering: Building and maintaining production-grade cloud infrastructure on AWS, Azure, or GCP for deploying ML applications, including drift monitoring.
- AI engineering: Applying current LLM and agentic-system techniques, including vector databases, LLM fine-tuning, AI guardrail systems, and deploying LLMs with Hugging Face, LangChain, and OpenAI.
- Graduate degree in a quantitative discipline such as Computer Science, Engineering, Statistics, or Operations Research, or equivalent practical experience.
- Ability to communicate and teach technical concepts to both technical and non-technical audiences.
- Passion for collaboration, continuous learning, and driving business value through ML and AI.
- Ability to meet technical training and role-specific expectations within three months of hire.
- Ability to travel up to 30% as needed.
Nice-to-haves
- 2+ years of customer-facing experience in a pre-sales or post-sales role.
Benefits
- Comprehensive benefits and perks are offered, with details varying by region.
Skills
Machine Learning, Generative AI, MLOps, AWS, Microsoft Azure, GCP, LLMs, RAG, AI Agents, Vector Databases, Hugging Face, LangChain, OpenAI, Model Monitoring, Drift Monitoring
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