Senior Specialist Solutions Architect
Architects production-grade machine learning and AI workloads for enterprise customers, with emphasis on GenAI, MLOps, distributed Spark systems, and cloud infrastructure. The role also supports technical sales, mentors colleagues, and requires 10+ years of industry experience plus customer-facing expertise.
About the job
Responsibilities
- Design and implement production-level machine learning 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, including retrieval-augmented generation (RAG) architectures, agentic systems with tool-calling, multi-agent orchestration and guardrails, AI observability, and natural-language querying of structured data.
- Support Solution Architects during technical sales cycles by building MVPs, leading deep-dive sessions, and aligning AI solutions with customer business 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 serve as a technical AI and machine learning thought leader.
Requirements
- 10+ years of hands-on industry data science or machine learning experience.
- Experience building and maintaining production-grade cloud infrastructure supporting machine learning applications and monitoring model performance, or applying advanced techniques in LLMs, agentic systems, vector databases, fine-tuning, and deployment tools.
- Hands-on experience with distributed Apache Spark-based systems.
- Experience with data engineering concepts.
- Pre-sales or post-sales experience with external clients across multiple industries.
- Proven ability to communicate and teach complex technical concepts to technical and non-technical audiences.
- Graduate degree in a quantitative discipline such as Computer Science, Engineering, Statistics, or Operations Research, or equivalent practical experience.
- Ability to complete technical training and role-specific outcomes within three months of hire.
- Ability to travel up to 30% when needed.
Nice to have
- 5+ years of customer-facing experience.
- Experience using Apache Spark to process large-scale distributed datasets.
- Experience with AWS, Azure, or Google Cloud.
- Passion for lifelong learning, collaboration, and delivering business value through AI.
Benefits
- Comprehensive benefits and perks are offered, with details varying by region.
Skills
Machine Learning, Data Science, Artificial Intelligence, LLMs, Generative AI, MLOps, Llmops, Spark, Data Engineering, RAG, Agentic Systems, Vector Databases, Hugging Face, LangChain, AWS
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