Staff AI Engineer

Generac; Ramban district; Sur place; INR 3 000 000 - 6 000 000; Plein temps

Résumé du poste

Generac seeks an experienced AI/ML architect to design and run production AI systems and agentic workflows. You will implement quality gates, monitoring, and observability to detect regressions and ensure reliability in real-world deployments.

You will collaborate with engineering, product, and design teams, mentoring engineers and translating evolving goals into concrete, measurable plans. Strong Python and CI/CD experience is essential.

Qualifications

• Bachelor's or Master's degree in CS, ML, or related field, or equivalent experience.
• 5+ years building and operating production AI/ML or software systems.

Responsabilités

• Architect, build, and operate production AI/ML systems and agentic workflows.
• Define quality and reliability practices for AI systems, including testing frameworks and monitoring.
• Instrument and operate production systems with logging, tracing, dashboards for visibility.
• Translate feedback into engineering improvements for models, prompts, retrievals, or system components.
• Define and track technical metrics (accuracy, latency, cost, reliability) and produce shareable artifacts.
• Establish standards, tooling, and patterns reused across teams building AI products.
• Partner with engineering, product, and design; mentor engineers; communicate trade-offs.
• Translate ambiguous goals into concrete, measurable engineering plans and deliverables.

Connaissances

Python

Distributed data pipelines

CI/CD

LLM-based system design

Evaluation/observability

Security/reliability

Cross-functional collaboration

Formation

Bachelor's or Master's in CS/ML or related field

Outils

LangGraph

Strands

Langfuse

LangSmith

Braintrust

Description du poste

• Architect, build, and operate production AI/ML systems and agentic workflows, including the tooling, infrastructure, and processes needed to develop, test, and deploy them reliably.
• Define and implement quality and reliability practices for AI systems — testing frameworks, monitoring, and pass/fail criteria that gate releases and catch regressions before they reach customers.
• Instrument and operate production systems, building sufficient visibility into system behavior (e.g., logging, tracing, dashboards) to diagnose issues and support continuous improvement.
• Translate production feedback and failures into concrete engineering improvements, driving iteration on models, prompts, retrieval, or other system components based on data and stakeholder input.
• Define and track the technical metrics that matter for a given system or product area (e.g., accuracy, latency, cost, reliability) and produce artifacts that product, engineering, and risk stakeholders can rely on.
• Establish technical standards, tooling, and patterns that can be reused across teams building AI-driven products, promoting consistency in how AI system quality is measured and maintained.
• Partner closely with engineering, product, and design; mentor other engineers; and serve as a technical resource on AI system design, quality, and reliability trade-offs.
• Translate ambiguous, evolving product and technical goals into concrete, measurable engineering plans and deliverables.

Minimum Job Requirements

Bachelor's or Master's degree in Computer Science, Machine Learning, or a related technical field, or equivalent experience.

Certification/License

None required.

Work Experience

5+ years building and operating production AI/ML or software systems, including hands-on experience with large language models (LLMs) and/or AI agent systems.

Knowledge, Skills & Abilities

• Strong software engineering fundamentals, including proficiency in Python and experience with distributed data pipelines and CI/CD.
• Demonstrated experience building and/or operating evaluation, testing, or observability capabilities for ML/AI systems in production.
• Experience with LLM-based system design, including techniques such as LLM-as-judge evaluation, golden-dataset curation, and regression/drift detection.
• Experience instrumenting or operating complex, multi-step systems (e.g., multi-step reasoning, tool use, retrieval, multi-agent handoffs).
• Experience assessing agentic or AI systems for security, reliability, or failure modes (e.g., red-teaming or similar practices).
• Ability to translate ambiguous product or technical goals into measurable, defensible engineering plans.
• Strong cross-functional collaboration and communication skills; able to represent technical trade-offs to non-technical stakeholders.

Preferred Job Requirements

• Experience with cloud AI/ML platforms (e.g., AWS Bedrock) and agent frameworks (e.g., LangGraph, Strands, or similar).
• Experience with cloud-native MLOps/LLMOps practices: packaging, CI/CD, A/B testing, and model or prompt versioning.
• Hands-on experience with evaluation or observability platforms (e.g., Langfuse, Braintrust, LangSmith, or similar).
• Track record of standing up a new technical capability (e.g., an evaluation or observability function) as a reusable, cross-team resource.
• Experience using synthetic data to increase test coverage for AI/ML systems.

Experience across multiple product domains or business contexts