Gen AI Engineer

• Build agentic AI systems: Design and implement tool-calling agents that combine retrieval, structured reasoning, and secure action execution (function calling, change orchestration, policy enforcement) following MCP protocol
• Productionize LLMs: Build evaluation framework for open-source and foundational LLMs
• implement retrieval pipelines, prompt synthesis, response validation, and self-correction loops tailored to production operations
• Integrate with runtime ecosystems: Connect agents to observability, incident management, and deployment systems to enable automated diagnostics, runbook execution, remediation, and post-incident summarization with full traceability
• Collaborate directly with users: Partner with production engineers, and application teams to translate production pain points into agentic AI roadmaps
• Safety, reliability, and governance: Build validator models, adversarial prompts, and policy checks into the stack
• Scale and performance: Optimize cost and latency via prompt engineering, context management, caching, model routing, and distillation
• Build a RAG pipeline: Curate domain-knowledge
• build data-quality validation framework
• establish feedback loops and milestone framework maintain knowledge freshness
• Raise the bar: Drive design reviews, experiment rigor, and high-quality engineering practices
• mentor peers on agent architectures, evaluation methodologies, and safe deployment patterns

• 5+ years of software development in one or more languages (Python, C/C++, Go, Java)
• strong hands-on experience building and maintaining large-scale Python applications preferred
• 3+ years designing, architecting, testing, and launching production ML systems, including model deployment/serving, evaluation and monitoring, data processing pipelines, and model fine-tuning workflows
• Practical experience with Large Language Models (LLMs): API integration, prompt engineering, fine-tuning/adaptation, and building applications using RAG and tool-using agents (vector retrieval, function calling, secure tool execution)
• Understanding of different LLMs, both commercial and open source, and their capabilities (e.g., OpenAI, Gemini, Llama, Qwen, Claude)
• Solid grasp of applied statistics, core ML concepts, algorithms, and data structures to deliver efficient and reliable solutions
• Strong analytical problem-solving, ownership, and urgency
• ability to communicate complex ideas simply and collaborate effectively across global teams with a focus on measurable business impact

Proficiency building and operating on cloud infrastructure (ideally AWS), including containerized services (ECS/EKS), serverless (Lambda), data services (S3, DynamoDB, Redshift), orchestration (Step Functions), model serving (SageMaker), and infra-as-code (Terraform/CloudFormation)