Artificial Intelligence Specialist
Hyrhub
- New York, New York, United States
- New York, New York, United States
Über
Key Responsibilities
Design end-to-end AI, ML, and GenAI solution approaches aligned with business objectives.
Translate complex pharmaceutical and business problems into AI-enabled solution designs.
Define solution components such as data inputs, AI models, GenAI workflows, APIs, user interfaces, orchestration layers, and deployment patterns.
Evaluate and recommend suitable AI models, LLMs, frameworks, cloud services, vector databases, and automation tools.
Create solution design documents, technical blueprints, architecture diagrams, and implementation roadmaps.
GenAI & Applied AI Solutioning
Design GenAI solutions using LLMs, RAG pipelines, prompt engineering, agents, knowledge search, summarization, automation, and workflow augmentation.
Identify opportunities where AI can improve productivity, decision‑making, analytics, and business processes.
Define build‑vs‑buy considerations for AI solutions and recommend appropriate technology stacks.
Develop proof‑of‑concepts or prototypes to validate solution feasibility.
Guide teams on responsible AI, explainability, model limitations, and risk mitigation.
Problem Framing & Business Translation
Work closely with business stakeholders to understand needs, pain points, workflows, and success criteria.
Convert business requirements into AI use cases, functional specifications, and technical solution designs.
Assess data availability, feasibility, complexity, risks, dependencies, and expected business impact.
Prioritize AI use cases based on value, feasibility, scalability, and adoption potential.
Communicate solution options, trade‑offs, risks, and recommendations to both technical and non‑technical audiences.
End‑to‑End Delivery Ownership
Own the solution lifecycle from ideation to design, prototype, implementation support, deployment, and adoption.
Partner with AI/ML engineers, data engineers, cloud architects, application teams, and platform teams to deliver production‑ready solutions.
Ensure solutions are scalable, secure, maintainable, and aligned with enterprise architecture standards.
Support productionization through MLOps, LLMOps, monitoring, governance, and continuous improvement practices.
Track business outcomes and ensure solutions deliver measurable impact.
Collaboration with Engineering & Platform Teams
Provide technical direction to development and engineering teams during implementation.
Collaborate on API design, data pipeline integration, model deployment, prompt management, vector search, and cloud architecture.
Ensure AI solutions are designed for reliability, performance, security, privacy, and compliance.
Review implementation approaches and help resolve design or integration challenges.
Act as a bridge between business teams, AI teams, and technology delivery teams.
Pharmaceutical Domain Application
Clinical trials
Real‑world evidence
Medical affairs
Drug discovery
Commercial analytics
Patient analytics
Regulatory and safety operations
Sales and marketing effectiveness
Understand pharma data types, workflows, compliance expectations, and business challenges.
Ensure AI solutions consider regulatory, data privacy, security, and ethical AI requirements.
Support use‑case design for regulated and sensitive healthcare environments.
Technical Expertise Required Skills
Strong understanding of AI, machine learning, GenAI, and applied analytics concepts.
Ability to design AI solutions without being limited to hands‑on model development.
Experience with Python and SQL.
Good understanding of ML models, statistical methods, deep learning, NLP, and LLM‑based systems.
Experience designing or working with LLMs and GenAI platforms, RAG architectures, vector databases, prompt engineering frameworks, AI agents and workflow automation, APIs and application integration patterns, cloud platforms such as AWS or Azure, MLOps or LLMOps practices.
Architecture & Solution Design Skills
Proven ability to design end‑to‑end AI/ML/GenAI systems.
Understanding of data pipelines, model serving, cloud deployment, orchestration, monitoring, and governance.
Ability to create solution architecture diagrams, technical design documents, and implementation plans.
Experience evaluating tools, models, platforms, and frameworks based on business and technical needs.
Understanding of scalability, security, privacy, performance, and maintainability considerations.
Pharmaceutical Domain Knowledge
Experience delivering AI, analytics, automation, or GenAI solutions in the pharmaceutical, healthcare, life sciences, or related industries.
Familiarity with pharma datasets, business processes, and industry challenges.
Understanding of compliance and privacy considerations such as GxP, HIPAA, GDPR, or similar frameworks is preferred.
Ability to engage with domain stakeholders and convert domain problems into practical AI solution designs.
Experience Requirements
6–8 years of experience in AI, data science, analytics, solution design, or technology consulting roles.
Demonstrated experience designing and delivering AI/ML or GenAI solutions.
Experience working with business stakeholders to define use cases and solution approaches.
Experience collaborating with engineering teams to implement and productionize AI solutions.
Prior experience in pharma, healthcare, or life sciences is strongly preferred.
Soft Skills
Strong solution‑oriented thinking and structured problem‑solving ability.
Excellent communication and stakeholder management skills.
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Sprachkenntnisse
- English
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