Agentic AI Engineer

We are seeking an experienced Agentic AI Engineer to design and implement GenAI agentic solutions that enhance reliability, reduce risk, and optimize cost in large-scale production environments.

This role focuses on building intelligent agents capable of diagnosing, reasoning, and executing actions in runtime ecosystems to support production operations and improve productivity.

• Agentic AI System Development:
  Design and implement tool-calling agents that integrate retrieval, structured reasoning, and secure action execution (e.g., function calling, policy enforcement) using MCP protocol.
• Engineer safety guardrails and enforce least-privilege access.
• LLM Productionization:
  Build evaluation frameworks for open-source and foundational LLMs.
• Implement retrieval pipelines, prompt synthesis, response validation, and self-correction loops tailored to production operations.
• Runtime Ecosystem Integration:
  Connect agents to observability, incident management, and deployment systems to enable automated diagnostics, runbook execution, remediation, and post-incident summarization with traceability.
• User
  
  Collaboration:
  
  Partner with production engineers and application teams to translate operational challenges into agentic AI roadmaps.
• Define objective functions linked to reliability, risk reduction, and cost efficiency.
• Safety, Reliability & Governance:
  Build validator models, adversarial prompts, and policy checks. Enforce deterministic fallbacks, circuit breakers, and rollback strategies.
• Instrument continuous evaluations for usefulness, correctness, and risk.
• Performance Optimization:
  Improve cost and latency through prompt engineering, context management, caching, model routing, and distillation.
• Use batching, streaming, and parallel tool-calls to meet service-level objectives under real-world load.
• RAG Pipeline Development:
  Curate domain knowledge, build data-quality validation frameworks, and establish feedback loops to maintain knowledge freshness.
• Engineering Excellence:
  Lead design reviews, promote rigorous experimentation, and mentor peers on agent architectures, evaluation methodologies, and safe deployment practices.

• 5+ years of software development experience in Python, C/C++, Go, or Java (Python preferred).
• 3+ years of experience designing and launching production ML systems, including model deployment, evaluation, monitoring, and fine-tuning workflows.
• Practical experience with LLMs: API integration, prompt engineering, fine-tuning, and building RAG-based applications with tool-using agents.
• Familiarity with commercial and open-source LLMs (e.g., OpenAI, Gemini, Llama, Qwen, Claude).
• Strong foundation in applied statistics, machine learning concepts, algorithms, and data structures.
• Excellent problem-solving and communication skills with a focus on measurable business impact.

• Experience with cloud infrastructure (preferably AWS), including ECS/EKS, Lambda, S3, Dynamo
  
  DB, Redshift, Step Functions, Sage Maker, and infrastructure-as-code tools like Terraform or Cloud Formation.
• Participation in platform modernization and performance engineering initiatives.