Machine Learning Architect
2 months ago
- 8-10 years of experience: Must have worked as a data engineer before venturing into AI/ML/Generative AI development.
- Coding experience: Proficient in a core programming language (Python, Java, Scala, Spark) and willingness to be partly billable in client projects.
- In-depth understanding: Of cloud platforms and various services on AWS/GCP and Azure, ML Platforms such as SageMaker, Vertex AI, and the latest GenAI services stack.
- Knowledge of: Machine Learning, Deep Learning, Generative AI/LLMs, and various use cases.
- Proficiency with: Big Data Analytics technologies, including hands-on expertise in building complex AI/ML and Data proofs-of-concept on public cloud platforms.
- Familiarity with visualization tools: Such as Tableau.
- Experience in: Discovering use cases, scoping, and delivering complex solution architecture designs to diverse audiences, adapting technical depth as needed.
- Understanding of: DataOps, MLOps, LLMOps, Observability, DevOps, and SRE concepts.
- Understanding of: LLM Prompts Engineering, LLM Finetuning, Open source LLM, and the latest tech innovations.
Client-facing skills and relationships:
- Engage customers in technical sales, confidently answering questions, guiding outcomes, and communicating technical value propositions.
- Enthusiasm for working with clients across HiTech industries (Information Services, Healthcare, Life Sciences, eBusiness, etc.) and gaining deep knowledge in these verticals.
- Ability to build strong customer relationships and collaborate internally with sales, delivery, and practice teams.
*Hands-on presales experience: In the AI and Data services industry. The ideal candidate would have collaborated with large analytics, AI, and ML consulting services organizations.
Additional desirable skills:
- Experience building demos for Generative AI.
- Exceptional communication and storytelling abilities.
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