AI Solutions Lead

About the RoleWe are seeking an experienced AI Solutions Lead to drive the design, development, and implementation of innovative AI solutions that deliver measurable business value. This role will lead the end-to-end lifecycle of AI initiatives, from identifying opportunities across business functions to deploying scalable, secure, and high-impact AI applications.The ideal candidate combines strong technical expertise in Generative AI, Agentic AI, and RAG architectures with the ability to align AI initiatives with organizational goals and operational needs.Key ResponsibilitiesDesign, build, and optimize Agentic AI workflows utilizing Large Language Models (LLMs).Develop and maintain Retrieval-Augmented Generation (RAG) pipelines and knowledge systems.Implement advanced prompt engineering, response control, and orchestration strategies.Lead the design, development, and deployment of AI solutions focused on:ForecastingPlanningReportingProcess optimizationEvaluate AI opportunities and translate business requirements into practical AI use cases.Develop AI roadmaps aligned with organizational objectives and digital transformation strategies.Conduct feasibility assessments and ROI analyses for AI initiatives.Manage relationships with external AI vendors and technology partners.Ensure AI solutions comply with data governance, security, privacy, and ethical standards.Monitor AI model performance and drive continuous improvement initiatives.Support the integration of AI systems with enterprise applications and business workflows.Stay current with emerging AI technologies and recommend innovative solutions.Required QualificationsEducationMaster's degree in Computer Science, Artificial Intelligence, Data Science, Software Engineering, or a related field.Experience5+ years of software engineering experience.4+ years of hands-on experience working with Generative AI and LLM-based solutions.Proven experience delivering AI solutions in production environments.Preferred CertificationsMLOps Fundamentals or ML Engineering Certification.RAG Systems Architecture Certification.Voice AI / Speech Recognition Certification (preferred).Technical SkillsGenerative AI & Agentic SystemsStrong hands-on experience with LLMs such as LLaMA, Mistral, GPT-family models, and similar technologies.Expertise in:Prompt EngineeringTool/Function CallingMulti-step ReasoningAgentic AI ArchitecturesExperience with AI orchestration frameworks including:LangChainLangGraphCrewAIAbility to design and manage multi-agent systems and task orchestration pipelines.RAG & Knowledge SystemsExperience building production-grade RAG solutions.Familiarity with embeddings technologies such as BGE and Instructor.Experience working with vector databases such as Qdrant or equivalent platforms.Voice AIExperience with Speech-to-Text (STT) and Text-to-Speech (TTS) systems is highly preferred.Integration & DevelopmentStrong experience integrating AI solutions with enterprise applications and backend services.Solid understanding of REST APIs and system architecture.Advanced proficiency in Python and software engineering best practices.Ability to write clean, scalable, and maintainable code.What Success Looks LikeSuccessful delivery of AI initiatives that improve operational efficiency and business performance.Adoption of scalable AI solutions across multiple business functions.Measurable ROI from AI investments.Secure, compliant, and ethical implementation of AI technologies.Continuous optimization and performance improvement of deployed AI systems.