AI Software Engineer

About the Role

Grade Level (for internal use): 10

Key Responsibilities

• Design and build agentic AI platform components including agents, tools, workflows, and integrations with internal systems.
• Implement observability across the AI lifecycle: tracing, logging, metrics, and evaluation pipelines to monitor agent quality, cost, and reliability.
• Translate business problems into agentic AI solutions by collaborating with product, SMEs, and platform teams on data, model, and orchestration requirements.
• Develop and maintain data pipelines, features, and datasets for training, evaluation, grounding, and safety of LLM-based agents.
• Lead experimentation and benchmarking: Testing of prompts, models, and agent workflows; analyze results and drive iterative improvements.
• Implement guardrails, safety checks, and policy controls across prompts, tool usage, access, and output filtering to ensure safe and compliant operation.
• Create documentation, runbooks, and best practices; mentor peers on agentic AI patterns, observability-first engineering, and data/ML hygiene.

Core Skills Required

• Strong programming experience in Python (preferred) or equivalent languages
• Solid understanding of LLM / GenAI fundamentals: prompting, embeddings, vector search, RAG, and basic agentic patterns (tool use, planning, orchestration).
• Experience running production systems or data pipelines on AWS / Azure / GCP, using containers, serverless, and managed storage/services.
• Hands-on familiarity with observability tools (OpenTelemetry, Prometheus, Grafana, ELK, etc.) across logs, metrics, and traces.
• Comfort working with structured and unstructured data; strong SQL plus experience with Pandas / Spark / dbt or similar frameworks.
• Ability to reason clearly about reliability, performance, and cost trade-offs.
• Strong collaboration and communication skills; ability to translate complex concepts for platform, product, data, security, and compliance teams.

Qualifications

• 26 years of experience in software engineering, data engineering, ML engineering, data science, MLOps roles.
• Bachelors or Masters degree in Computer Science, Engineering, Data Science, or equivalent practical experience.
• Experience with CI/CD, code reviews, and modern engineering best practices.
• Nice to Have:
  • Exposure to agentic AI frameworks (LangChain, LangGraph, OpenAI Agents, etc.)
  • Experience with LLM observability, eval frameworks, or prior work on production LLM/agent systems.

What We’re Looking For

• Build for scale: Think like platform builders and design systems that work across teams, not just for todays use case.
• Lead with observability: Instrument first, debug with data, and deliver dashboards that reveal the truth.
• Ship safely: Never deploy without guardrails or validations, even if it adds upfront effort.
• Make thoughtful trade-offs: Clearly articulate decisions around cost, quality, latency, and reliability.
• Own the end-to-end stack: Move comfortably between data pipelines, agent logic, infrastructure, and production monitoring.
• Learn through experimentation: Test ideas, study failures, iterate rapidly, and improve continuously.
• Communicate with impact: Explain complex AI concepts in simple, business-relevant terms to technical and non-technical stakeholders.
• Stay ahead of the curve: Actively explore emerging technologies like LangGraph, agentic frameworks, and new LLM capabilities.