Contract opportunity to design, build, and deploy secure, reliable agentic AI systems for production diagnostics, automation, and remediation.

Design and implement tool-calling agents that combine retrieval, structured reasoning, and secure action execution using Model Context Protocol (MCP).
Implement function calling, change orchestration, policy enforcement, and least-privilege access controls.
Build evaluation frameworks for open-source and foundation LLMs, including prompt synthesis, response validation, and self-correction loops.
Integrate agents with observability, incident management, and deployment systems to support automated diagnostics, runbook execution, remediation, and post-incident summaries with full traceability.
Collaborate with production engineers and application teams to define agentic AI roadmaps and measurable objectives tied to reliability, risk reduction, and cost.
Implement validator models, adversarial prompts, policy checks, deterministic fallbacks, circuit breakers, and rollback strategies.
Build RAG pipelines, curate domain knowledge, validate data quality, and establish feedback loops and milestones to maintain knowledge freshness.

5+ years of software development experience in one or more languages, including Python, C/C++, Go, or Java.
3+ years designing, architecting, testing, and launching production ML systems, including model deployment and serving, evaluation, monitoring, data processing pipelines, and fine-tuning workflows.
Practical LLM experience with API integration, prompt engineering, fine-tuning or adaptation, and applications using retrieval-augmented generation (RAG) and tool-using agents.
Experience with vector retrieval, function calling, and secure tool execution.
Understanding of commercial and open-source LLMs and capabilities, including OpenAI, Gemini, Llama, Qwen, and Claude.
Solid understanding of applied statistics, core ML concepts, algorithms, and data structures.
Strong analytical problem-solving, ownership, and urgency, with a focus on measurable business impact.
Ability to communicate complex ideas clearly and collaborate effectively across global teams.

Strong hands-on experience building and maintaining large-scale Python applications.
Proficiency building and operating cloud infrastructure, ideally AWS.
Experience with containerized services using ECS/EKS, serverless computing using Lambda, and data services including S3, DynamoDB, and Redshift.
Experience with Step Functions, SageMaker, and Infrastructure as Code using Terraform or CloudFormation.

Senior AI Engineer

Job Description