Senior Generative AI Developer / Lead AI Engineer

Senior Generative AI Developer (10+ Years Experience) Position Title

Senior Generative AI Developer / Lead AI Engineer
Location

Remote CST Timezone
Job Summary

We are seeking a highly experienced Senior Generative AI Developer with 10+ years of professional experience in Software Development and/or Data Analytics, including a minimum of 3 years of hands-on experience developing Generative AI solutions.

The ideal candidate will be responsible for designing, developing, and deploying enterprise-scale AI applications powered by Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), AI Agents, and cloud-native AI services. The candidate must possess deep expertise in software engineering principles, AI solution architecture, model integration, prompt engineering, and production-grade AI systems.

This role requires a strong combination of technical leadership, innovation, problem-solving, and collaboration skills to deliver transformative AI solutions that solve complex business challenges.
Required Experience

• Minimum 10 years of experience in Software Development, Data Engineering, Data Science, Analytics, or related technology disciplines.
• Minimum 3 years of recent, continuous, hands-on experience developing Generative AI solutions.
• Proven experience delivering AI solutions from concept through production deployment.
• Experience designing scalable, secure, and enterprise-grade applications.

Key Responsibilities Generative AI Development

• Design and develop Generative AI applications utilizing Large Language Models (LLMs).
• Build intelligent assistants, copilots, conversational AI solutions, and AI agents.
• Implement Retrieval-Augmented Generation (RAG) architectures.
• Develop prompt engineering frameworks and optimize model performance.
• Integrate foundation models from OpenAI, Azure OpenAI, Anthropic Claude, Gemini, Llama, Mistral, or equivalent.

Solution Architecture

• Design scalable AI solution architectures for enterprise environments.
• Define AI application patterns, integration approaches, and deployment strategies.
• Evaluate model suitability, performance, cost, security, and business value.

Software Engineering

• Design and develop robust APIs, microservices, and cloud-native applications.
• Follow modern software engineering practices including CI/CD, DevSecOps, testing, and code quality standards.
• Build reusable AI frameworks and accelerators.

Data & Knowledge Engineering

• Design and implement vector databases and semantic search solutions.
• Build data pipelines supporting AI applications.
• Develop document ingestion, indexing, and retrieval mechanisms.

AI Operations (LLMOps/MLOps)

• Implement model monitoring, evaluation, and governance frameworks.
• Manage deployment pipelines for AI solutions.
• Monitor performance, costs, reliability, and security of deployed AI systems.

Leadership & Collaboration

• Provide technical leadership and mentoring to development teams.
• Collaborate with architects, product owners, data scientists, and business stakeholders.
• Participate in technical reviews, architectural discussions, and innovation initiatives.

Required Technical Skills Programming

• Python (Expert)
• Java or C#
• JavaScript / TypeScript
• SQL

Generative AI Technologies

• Large Language Models (LLMs)
• Prompt Engineering
• Retrieval-Augmented Generation (RAG)
• Agentic AI Frameworks
• Fine-Tuning and Model Evaluation
• Embeddings and Vector Search
• Knowledge Graphs (preferred)

AI Frameworks

• LangChain
• LangGraph
• LlamaIndex
• Semantic Kernel
• AutoGen or equivalent Agent Frameworks

Cloud Platforms

• Microsoft Azure AI Services / Azure OpenAI
• AWS Bedrock
• Google Vertex AI

Databases

• PostgreSQL
• Cosmos DB
• MongoDB
• Pinecone
• Weaviate
• ChromaDB
• FAISS

DevOps & Deployment

• Docker
• Kubernetes
• GitHub Actions
• Azure DevOps
• Terraform
• CI/CD Pipelines

Required Knowledge Areas

• Natural Language Processing (NLP)
• Deep Learning Fundamentals
• Transformer Architectures
• Responsible AI
• AI Security
• Model Governance
• Data Privacy and Compliance
• Enterprise Architecture

Preferred Qualifications

• Master's Degree in Computer Science, Artificial Intelligence, Data Science, Engineering, or related field.
• Azure AI Engineer Associate Certification.
• AWS Machine Learning Specialty Certification.
• Google Cloud Generative AI Certification.
• Experience working in regulated industries such as Banking, Healthcare, Insurance, Retail, or Telecommunications.
• Experience building multi-agent systems and autonomous workflows.