Postdoctoral Researcher focusing on privacy technologies and AI security at Fraunhofer Institute. Engaging in research, mentoring, and publishing in leading cryptography venues.
About the role
- Strategic Accounts AI Growth Consultant managing AI and data solutions for healthcare strategic accounts. Leading technical development and collaborating with account management and delivery teams in the process.
Responsibilities
- Lead hands‑on development of reference architectures, POCs, and solution blueprints across data engineering, cloud data platforms, and applied ML.
- Work closely with onsite Growth Partners to design scalable AI/analytics solutions aligned to client outcomes.
- Assess client environments, pain points, and business priorities, then translate these into solution architectures and transformation roadmaps.
- Own the technical components of RFP/RFI responses, solution narratives, cost models, and implementation plans.
- Build reusable assets (demos, templates, frameworks) to accelerate pre‑sales cycles.
- Participate in client workshops, requirement discovery, and technical deep‑dives to shape opportunities.
- Provide hands-on expertise across cloud data ecosystems (Azure/AWS/GCP), modern data stacks, ETL/ELT patterns, and data quality frameworks.
- Support development of accelerators and internal IP, including data pipelines, MLOps workflows, or analytics modules.
- Work with engineering and analytics teams to size effort, identify risks, and recommend best-practice implementation patterns.
- Serve as the offshore partner to U.S.-based Strategic Account Growth Partners.
- Align on deal strategies, value propositions, and account expansion priorities.
- Support creation of client-ready materials: architecture diagrams, impact models, POVs, and technical storytelling artifacts.
- Ensure solution designs transition smoothly into delivery by providing clarity on architecture, dependencies, and success criteria.
- Participate in early delivery phases (e.g., backlog creation, sprint planning, validation) to ensure solution fidelity.
- Act as a technical SME during initial implementation cycles to ensure client satisfaction.
Requirements
- 5–8 years of experience in data engineering, cloud data platforms, analytics, or applied AI/ML.
- Strong hands‑on experience with:
- Python / SQL
- Data pipelines (e.g., ADF, Airflow, dbt, Glue, Databricks, Snowflake, BigQuery, Redshift, Synapse)
- Cloud architecture (Azure/AWS/GCP)
- MLOps / model deployment workflows (nice-to-have)
- Ability to create architectural diagrams, POCs, and performance‑optimized data solutions
- Prior experience as a Solutions Architect, Data Architect, or Analytics Pre‑Sales Engineer.
- Strong capability in crafting proposals, presenting technical solutions, and engaging with technical and business stakeholders.
- Ability to articulate complex technical concepts in simple, value-oriented language.
- Strong communication and storytelling ability.
- Ability to collaborate across time zones with account, delivery, and capability teams.
- High comfort with ambiguity; ability to structure solutions from incomplete or evolving requirements.
- Experience working with U.S. healthcare clients.
- Exposure to enterprise AI/ML use cases, data governance, or cloud modernization programs.
- Certifications in cloud platforms (Azure/AWS/GCP) or Databricks
Job title
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