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.

Requirements

• 2-6 years of experience in software engineering, data engineering, ML engineering, data science, MLOps roles.
• Bachelor's or Master's 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.
• Beyond skills and experience, we want engineers who:
• Build for scale: Think like platform builders and design systems that work across teams, not just for today's 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.
• What's In It For You?
• Our Mission:
• Advancing Essential Intelligence.
• Our People:
• Our Values:
• Integrity, Discovery, Partnership

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