GenAI LLM Engineer

Position Title: Gen AI LLM EngineerPosition Description:Position Overview We are seeking a solution-oriented engineer who can identify where AI/LLM capabilities can transform business operations and architect practical integrations within enterprise software systems. This is not a traditional ML engineering or data science role focused on model training and deployment infrastructure.Key Responsibilities :AI Solution Strategy & InnovationIdentify opportunities where AI/LLM integration can solve business problems and add valueEvaluate emerging AI technologies (GPT, Claude, open-source LLMs, agent frameworks) for enterprise applicabilityAssess and recommend appropriate AI tools and frameworks based on use case requirements and technical constraintsDesign solution architectures that integrate AI capabilities into existing software products · Build business cases and technical proposals for AI initiativesStay current with AI/LLM developments and translate them into actionable solutions Hands-On Development & IntegrationBuild production-ready integrations between enterprise applications and AI/LLM servicesDevelop RESTful APIs, microservices, and middleware for AI feature deliveryImplement prompt engineering strategies, RAG systems, and AI agent workflows · Create proof-of-concepts to validate AI use cases efficientlyWrite clean, maintainable, testable code that follows enterprise standardsWork with vector databases, embeddings, and semantic search implementations Required QualificationsExperience Profile5+ years of enterprise software development experience2+ years actively working with AI/LLM integration in software applications ·Proven track record of identifying opportunities and delivering solutions (not just executing requirements)Experience building and consuming RESTful APIs, microservices architectures · Strong understanding of cloud platforms (AWS/GCP/Azure)Hands-on coding experience in Java and Python AI/LLM KnowledgePractical experience integrating LLMs (GPT, Claude, Gemini, or open-source models) into applicationsUnderstanding of prompt engineering techniques (zero-shot, few-shot, chain-of-thought) · Familiarity with RAG (Retrieval Augmented Generation) patterns and vector search · Experience with at least one AI framework or SDK (LangChain, LlamaIndex, OpenAI SDK, etc.) · Knowledge of AI agent patterns and agentic workflows Technical Foundation · Strong object-oriented programming and design patternsExperience with enterprise frameworks (Spring Boot, similar)Database experience (SQL, NoSQL, Vector databases)API design and integration patterns · Git, CI/CD, and modern development workflows · Cloud-native development experience Soft Skills · Solution-focused mindset - you see possibilities, not just problems · Strong communication skills to bridge technical and business stakeholders · Comfortable with ambiguity and rapid technology evolutionSkills Required:LLM, AI, Google Cloud Platform - Biq Query, Data Flow, Dataproc, Data Fusion, TERRAFORM, Tekton,Cloud SQL, AIRFLOW, POSTGRES, Airflow PySpark, Python, APIExperience Required:Specialist Exp: 5+ experience in relevant fieldExperience Preferred:Hands on experience in building AI solutions to solve business problems across the enterpriseEducation Required:Bachelor's DegreeEducation Preferred:Certification ProgramAdditional Safety Training/Licensing/Personal Protection Requirements:Additional Information :Key Responsibilities AI Solution Strategy & InnovationIdentify opportunities where AI/LLM integration can solve business problems and add value Evaluate emerging AI technologies (GPT, Claude, open-source LLMs, agent frameworks) for enterprise applicability · Assess and recommend appropriate AI tools and frameworks based on use case requirements and technical constraintsDesign solution architectures that integrate AI capabilities into existing software productsBuild business cases and technical proposals for AI initiativesStay current with AI/LLM developments and translate them into actionable solutions Hands-On Development & IntegrationBuild production-ready integrations between enterprise applications and AI/LLM servicesDevelop RESTful APIs, microservices, and middleware for AI feature delivery · Implement prompt engineering strategies, RAG systems, and AI agent workflows · Create proof-of-concepts to validate AI use cases efficiently · Write clean, maintainable, testable code that follows enterprise standards · Work with vector databases, embeddings, and semantic search implementations