Artificial Intelligence Engineer

Job Description

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 Require

• dStrong programming experience in Python (preferred) or equivalent language
• sSolid 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

.
Qualificatio

• ns5–6 years of experience in software engineering, data engineering, ML engineering, data science, MLOps role
• s.Bachelor's or Master's degree in Computer Science, Engineering, Data Science, or equivalent practical experienc
• e.Experience with CI/CD, code reviews, and modern engineering best practice
• s.Nice to Hav
• e:Exposure to agentic AI frameworks (LangChain, LangGraph, OpenAI Agents, etc
• .)Experience with LLM observability, eval frameworks, or prior work on production LLM/agent system

s.What We're Looking F

orBeyond skills and experience, we want engineers wh

o:
Build for scale: Think like platform builders and design systems that work across teams, not just for today's use ca

se.Lead with observability: Instrument first, debug with data, and deliver dashboards that reveal the tru

th.Ship safely: Never deploy without guardrails or validations, even if it adds upfront effo

rt.Make thoughtful trade-offs: Clearly articulate decisions around cost, quality, latency, and reliabili

ty.Own the end-to-end stack: Move comfortably between data pipelines, agent logic, infrastructure, and production monitori

ng.Learn through experimentation: Test ideas, study failures, iterate rapidly, and improve continuous

ly.Communicate with impact: Explain complex AI concepts in simple, business-relevant terms to technical and non-technical stakeholde

rs.Stay ahead of the curve: Actively explore emerging technologies like LangGraph, agentic frameworks, and new LLM capabiliti