[Remote] Generative AI Engineer
Note: The job is a remote job and is open to candidates in USA. Hyrhub is a company focused on AI solutions, and they are seeking a Generative AI Engineer to design and implement AI solutions aligned with business objectives. The role involves collaborating with various teams to create scalable and secure AI applications, particularly in the pharmaceutical domain, while ensuring compliance with regulatory standards.
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
- 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
- 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
- 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
- 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
- Apply AI and GenAI solutions across pharmaceutical domains such as clinical trials, real-world evidence, medical affairs, drug discovery, commercial analytics, patient analytics, regulatory and safety operations, and 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
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
- Experience designing or working with RAG architectures
- Experience designing or working with vector databases
- Experience designing or working with prompt engineering frameworks
- Experience designing or working with AI agents and workflow automation
- Experience designing or working with APIs and application integration patterns
- Experience designing or working with cloud platforms such as AWS or Azure
- Experience designing or working with MLOps or LLMOps practices
- 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
- 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
- Ability to engage with domain stakeholders and convert domain problems into practical AI solution designs
- 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
- Strong solution-oriented thinking and structured problem-solving ability
- Excellent communication and stakeholder management skills
- Understanding of compliance and privacy considerations such as GxP, HIPAA, GDPR, or similar frameworks
- Prior experience in pharma, healthcare, or life sciences
Company Overview