Posted:3 days ago|
Platform:
On-site
Full Time
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.
AI Solution Strategy & Innovation
Identify 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 constraints
Design solution architectures that integrate AI capabilities into existing software products · Build business cases and technical proposals for AI initiatives
Stay current with AI/LLM developments and translate them into actionable solutions Hands-On Development & Integration
Build production-ready integrations between enterprise applications and AI/LLM services
Develop 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 Required Qualifications
5+ years of enterprise software development experience
2+ 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 Knowledge
Practical experience integrating LLMs (GPT, Claude, Gemini, or open-source models) into applications
Understanding 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 patterns
Experience 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 evolution
Skills Required:
LLM, AI, Google Cloud Platform - Biq Query, Data Flow, Dataproc, Data Fusion, TERRAFORM, Tekton,Cloud SQL, AIRFLOW, POSTGRES, Airflow PySpark, Python, API
Specialist Exp: 5+ experience in relevant field
Hands on experience in building AI solutions to solve business problems across the enterprise
Bachelor's Degree
Certification Program
Additional Safety Training/Licensing/Personal Protection Requirements:
Key Responsibilities AI Solution Strategy & Innovation
Identify 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 constraints
Design solution architectures that integrate AI capabilities into existing software products
Build business cases and technical proposals for AI initiatives
Stay current with AI/LLM developments and translate them into actionable solutions Hands-On Development & Integration
Build production-ready integrations between enterprise applications and AI/LLM services
Develop 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
The Judge Group
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