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 protected and compliant operation.
- Create documentation, runbooks, and best practices; mentor peers on agentic AI patterns, observability-first engineering, and data/ML hygiene.

Requirements




- 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
- 2–6 years of experience in software engineering, data engineering, ML

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