Senior Specialist Solutions Architect
This senior customer-facing architect designs and delivers production ML and generative AI workloads, supports technical sales, and influences AI platform strategy. The role requires 10+ years of data science or machine learning experience, distributed Spark expertise, and strong client communication skills.
About the job
Responsibilities
- Design and implement production-level ML and AI workloads, including end-to-end pipelines, training and inference optimization, MLOps lifecycle management, and integration with cloud-native services.
- Lead enterprise generative AI solutions involving RAG architectures, agentic systems, tool calling, multi-agent orchestration, guardrails, AI observability, and natural-language querying of structured data.
- Provide advanced technical support to Solution Architects during technical sales cycles by building MVPs, leading deep-dive sessions, and aligning AI solutions with complex customer challenges.
- Collaborate with product and engineering teams to represent customer needs, define priorities, and influence the platform AI roadmap.
- Create technical tutorials and training materials, present at industry conferences, lead hackathons, and drive AI platform adoption.
- Mentor colleagues and contribute technical thought leadership.
Requirements
- 10+ years of hands-on industry data science or machine learning experience.
- Experience building and maintaining production-grade cloud infrastructure using AWS, Azure, or GCP for ML application deployment and model-performance monitoring; or experience applying advanced techniques in LLMs, agentic systems, vector databases, fine-tuning, and deployment tools.
- Hands-on experience with distributed Spark-based systems and large-scale distributed datasets.
- Experience with data engineering concepts.
- Pre-sales or post-sales experience working with external clients across multiple industries; 5+ years of customer-facing experience preferred.
- Ability to communicate and teach complex technical concepts to technical and non-technical audiences.
- Ability to complete technical training and role-specific outcomes within 3 months of hire.
- Ability to travel up to 30% when needed.
Preferred Qualifications
- Experience working with Apache Spark to process large-scale distributed datasets.
- Graduate degree in a quantitative discipline such as Computer Science, Engineering, Statistics, or Operations Research, or equivalent practical experience.
- Passion for lifelong learning, collaboration, and driving business value through AI.
Benefits
- Comprehensive benefits and perks are offered, with details varying by region.
Skills
Machine Learning, Generative AI, MLOps, Llmops, RAG, AI Agents, Vector Databases, AWS, Azure, GCP, Spark, Data Engineering, Hugging Face, LangChain, Fine-Tuning
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