Applied AI Engineer (Agentic Systems & Reputation Intelligence)
New
B
Built InHR Tech
IndiaFull-TimeMiddle
Salary not disclosed
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Job Details
- Required Skills
- PythonC#.NETReactAWS Lambda
Requirements
- Deep experience designing and shipping autonomous systems using patterns like ReAct, Planning, Reflection, and Multi-Agent Orchestration.
- Expert-level implementation experience with frontier model APIs from OpenAI, Google, and Perplexity, including function calling, structured outputs, and streaming.
- Proficiency with agentic developer tools (e.g., Claude Code, Cursor, or custom agentic CLI workflows).
- Demonstrated ability to manage multiple autonomous agents across a codebase: writing, testing, and auditing code with minimal manual intervention.
- Strong proficiency in Python for AI orchestration.
- Working knowledge of C# / .NET for integration with core platform.
- Practical experience deploying and operating serverless architectures on AWS (Lambda, Step Functions, EventBridge) for high-frequency, agentic workloads.
- Experience building custom evaluation frameworks (LLM-as-a-Judge, trace-based testing, regression suites) to maintain and improve agent output quality over time.
Responsibilities
- Architect Agentic Systems: Design and implement production-grade agentic workflows—including Reflection, Self-Correction, Planning, and Multi-Agent Orchestration patterns—to power a growing portfolio of AI-driven product capabilities.
- Operationalize Agents in Production: Own the full lifecycle from rapid prototype to production deployment. Build agent systems with real-world constraints in mind: token budgets, latency targets, graceful degradation, and cost observability.
- Evolve Multi-Model Intelligence: Develop systems that synthesize outputs from ChatGPT, Gemini, and Perplexity to identify sentiment discrepancies, detect brand hallucinations, and surface actionable intelligence for customers.
- Build Scalable Cloud Infrastructure: Design and maintain serverless backend services using Python and AWS Lambda, ensuring agents perform efficiently at scale with proper observability.
- Drive Evaluation and Quality: Build LLM-as-a-Judge evaluation frameworks, trace-based testing pipelines, and quality feedback loops that keep agent output reliable as models and prompts evolve.
- Collaborate Across Teams: Work closely with Software Engineers and Product Managers to integrate agentic behaviors into production frameworks, ensuring AI systems are stateful, observable, and resilient.
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