AI Specialist

AI Specialist – Solution Design & GenAI Location: East Coast, United States Experience - 8+ years Brief We are a tech-driven staffing and recruiting firm based in North America. Delivering the top 1% of Data, Analytics, AI and Sales talent for companies ranging from startups to enterprise. Client Brief Founded in 2004, we are a global digital solutions partner trusted by leading Fortune 500 companies across industries such as pharmaceuticals & healthcare, retail, and BFSI. As an analytics intelligence c ompany , we specialize in data & analytics, data engineering, machine learning, AI, and automation to help organizations streamline operations and unlock measurable business value. As part of our team, you will collaborate with industry-leading experts to deliver cutting-edge AI solutions that solve real-world business challenges. What We Offer Opportunity to work with globally recognized brands Continuous learning and upskilling opportunities Flexible hybrid work model Recognition and rewards for great work Strong culture of leadership development from within Our core values — Agility, Collaboration, Client Focus, Innovation, and Integrity — guide every decision we make. Role Overview In this role, you will play a key part in empowering clients through data-driven insights and innovative AI-powered digital solutions. You will design scalable AI/ML/GenAI solutions, translate complex business problems into technical architectures, and support end-to-end implementation across enterprise environments. Key Responsibilities 1. AI Solution Design & Architecture Design end-to-end AI, ML, and GenAI solution architectures aligned with business objectives. Translate complex pharmaceutical and business problems into AI-enabled solution designs. Define solution components including: Data inputs AI/ML models GenAI workflows APIs User interfaces Orchestration layers Deployment patterns Evaluate and recommend suitable: AI models and LLMs Frameworks and cloud services Vector databases Automation tools Create technical blueprints, architecture diagrams, solution documents, and implementation roadmaps. 2. GenAI & Applied AI Solutioning Design GenAI solutions using: Large Language Models (LLMs) Retrieval-Augmented Generation (RAG) Prompt engineering AI agents Knowledge search Summarization workflows Process automation Identify opportunities where AI can improve: Productivity Decision-making Analytics Business processes Perform build-vs-buy analysis for AI platforms and tools. Develop proof-of-concepts (POCs) and prototypes to validate feasibility. Guide teams on: Responsible AI Explainability Model limitations Risk mitigation 3. Problem Framing & Business Translation Collaborate closely with business stakeholders to understand: Pain points Workflows Requirements Success criteria Convert business requirements into: AI use cases Functional specifications Technical solution designs Assess: Data availability Feasibility Complexity Risks and dependencies Expected business impact Prioritize AI use cases based on: Business value Feasibility Scalability Adoption potential Present solution trade-offs, recommendations, and risks to both technical and non-technical audiences. 4. End-to-End Delivery Ownership Own the AI solution lifecycle from ideation to deployment and adoption. Partner with: AI/ML engineers Data engineers Cloud architects Application teams Platform teams Ensure solutions are: Scalable Secure Maintainable Aligned with enterprise architecture standards Support productionization through: MLOps LLMOps Monitoring Governance Continuous improvement Track measurable business outcomes and ensure solution impact. 5. Collaboration with Engineering & Platform Teams Provide technical direction during implementation. Collaborate on: API design Data pipelines Model deployment Prompt management Vector search Cloud architecture Ensure solutions meet standards for: Reliability Performance Security Privacy Compliance Review implementation approaches and resolve integration/design challenges. Serve as a bridge between business teams, AI teams, and engineering teams. 6. Pharmaceutical Domain Application Apply AI and GenAI solutions across pharmaceutical and healthcare domains such as: Clinical trials Real-world evidence Medical affairs Drug discovery Commercial analytics Patient analytics Regulatory and safety operations Sales and marketing effectiveness Additional Responsibilities Understand pharma datasets, workflows, and compliance expectations. Ensure adherence to: Regulatory requirements Data privacy standards Ethical AI practices Design AI use cases for regulated healthcare environments. Technical Expertise Required Skills Strong understanding of: Artificial Intelligence (AI) Machine Learning (ML) Generative AI (GenAI) Applied analytics Proficiency in: Python SQL Knowledge of: Statistical methods Deep learning NLP LLM-based systems Experience with: LLMs and GenAI platforms RAG architectures Vector databases Prompt engineering frameworks AI agents and workflow automation APIs and integration patterns AWS and/or Azure cloud platforms MLOps and LLMOps practices Architecture & Solution Design Skills Proven experience designing enterprise AI/ML/GenAI systems. Understanding of: Data pipelines Model serving Cloud deployment Orchestration Monitoring Governance Ability to create: Architecture diagrams Technical design documentation Implementation plans Experience evaluating tools, platforms, and frameworks based on business and technical requirements. Strong understanding of: Scalability Security Privacy Performance Maintainability Pharmaceutical Domain Knowledge Experience delivering AI, analytics, automation, or GenAI solutions within: Pharmaceuticals Healthcare Life Sciences Familiarity with pharma business processes and datasets. Understanding of compliance frameworks such as: GxP HIPAA GDPR Ability to engage with domain stakeholders and translate domain problems into practical AI solutions. Experience Requirements 6–8 years of experience in: AI Data Science Analytics Solution Design Technology Consulting Proven experience delivering AI/ML/GenAI solutions. Experience collaborating with stakeholders to define use cases and solution approaches. Experience working with engineering teams to productionize AI systems. Prior experience in pharmaceutical, healthcare, or life sciences industries is strongly preferred. Soft Skills Strong analytical and structured problem-solving skills. Excellent communication and stakeholder management abilities. Ability to explain AI concepts and solution trade-offs to non-technical audiences. Comfortable working in fast-paced, ambiguous, and cross-functional environments. Strong ownership mindset with the ability to drive initiatives from concept to execution. Ability to mentor, guide, and influence both technical and business teams. Equal Opportunity Employer We are committed to fostering an inclusive workplace free from discrimination and harassment. We believe diversity drives innovation and strengthens our ability to deliver impactful solutions. Show more Show less