Ultimate Peaks - Hiring for a Futuristic Client
We are seeking a
Senior Data & AI Solutions Consultant
to lead presales and delivery for advanced Data, AI, and Generative AI initiatives. This hybrid role combines client engagement, solution architecture, and end-to-end project delivery across modern data platforms and AI ecosystems.
Key Responsibilities
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Engage with clients to understand business challenges and translate them into AI, GenAI, ML, and data platform use cases.
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Design and present end-to-end solution architectures, including data ingestion, processing, modeling, storage, and analytics layers.
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Prepare technical proposals, SOWs, effort estimates, pricing sheets, and presales documentation for RFPs.
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Lead and support RFP responses, demos, and PoCs across AI, Generative AI, and data analytics workloads.
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Oversee project delivery from discovery, architecture, and implementation to testing, deployment, and handover.
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Collaborate with data engineers, cloud architects, ML engineers, and cross-functional teams to ensure scalable and secure solutions.
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Utilize cloud data and AI platforms (AWS, Azure, GCP) to build data lakes, data warehouses, ETL/ELT pipelines, and real-time data flows.
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Develop and support Generative AI solutions using LLMs, RAG architectures, vector databases, embeddings, and prompt engineering.
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Ensure delivery quality, documentation completeness, and adherence to architecture best practices.
Requirements
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7-12 years of experience in Data & AI solutioning, architecture, and delivery, with strong presales exposure.
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2-3 years hands-on experience in AI, Machine Learning, LLMs, or Generative AI.
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Proficiency in Python, SQL, and modern ML/AI frameworks.
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Proven ability to design and deliver data platforms including data lakes, data warehouses, and ETL/ELT pipelines.
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Strong experience preparing technical proposals, estimates, and project plans.
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Strong communication and presentation skills with the ability to work across business and technical teams.
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Demonstrated experience delivering end-to-end AI and data projects, including feature engineering, model development, deployment, and API integration.
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Experience with cloud ecosystems (AWS/Azure/GCP) and modern data stack technologies.