GenAI Engineer

GenAI Engineer

Location: Remote
Duration: Long-Term Contract with Potential Extensions
Openings: 1

Job Description:
We are seeking a seasoned GenAI Engineer with strong software engineering fundamentals and hands-on experience building and deploying production-grade Generative AI solutions.

Required Skills & Experience:

• 10+ years of software engineering experience with strong knowledge of distributed systems, APIs, data pipelines, and cloud-native architecture.
• Strong end-to-end SDLC ownership, including design, development, testing, CI/CD, deployment, and production support.
• Extensive cloud experience; Google Cloud Platform preferred, including managed services, IAM, networking, event-driven architecture, and Infrastructure as Code.
• 3+ years of hands-on Generative AI/LLM experience in production environments.
• Strong experience designing and building multi-agent systems using LangGraph, AutoGen, CrewAI, or equivalent frameworks.
• Deep RAG experience, including embeddings, vector databases, hybrid search, chunking, re-ranking, and evaluation.
• Knowledge of Transformer architecture, attention mechanisms, tokenization, context windows, prompting, and fine-tuning.
• Experience with LLM evaluation, observability, guardrails, and cost optimization.
• Strong CI/CD and production engineering experience.
• Excellent analytical, communication, and problem-solving skills.
• Self-driven technical leader who can work independently and influence architecture and engineering decisions.
• Strong hands-on coding experience; this is an individual contributor engineering role.

Key Responsibilities:

• Architect and develop production-grade GenAI, RAG, and multi-agent solutions.
• Build and operate AI systems from ingestion and vector storage through agent orchestration and production deployment.
• Establish best practices for LLM integration, prompt engineering, context management, evaluation, and observability.
• Own the complete SDLC for AI initiatives.
• Translate complex business requirements into scalable AI architectures.
• Serve as a technical leader and hands-on engineering resource across AI initiatives.