Note: The job is a remote job and is open to candidates in USA. Photon is seeking a Senior Agentic AI Engineer to design, build, deploy, and operate enterprise-grade AI agents and multi-agent systems. The role focuses on Generative AI, LLMs, and AI orchestration, delivering scalable copilots and intelligent automation solutions.

Responsibilities

• Design, develop, and deploy AI agents and multi-agent workflows using Python and agentic frameworks (e.g., LangChain, LangGraph, or equivalent)
• Build enterprise-scale RAG solutions over structured and unstructured data sources
• Develop agent orchestration workflows integrating models, tools, APIs, and enterprise services
• Build AI-powered copilots, assistants, and automation solutions for enterprise use cases
• Implement AI monitoring, observability, tracing, telemetry, and performance measurement
• Implement agent memory patterns (short-term, long-term, episodic)
• Integrate agents with enterprise platforms (APIs, databases, SharePoint, Confluence, Salesforce, knowledge repositories)
• Contribute to AI governance: guardrails, security controls, policy enforcement, and compliance
• Optimize prompts, reasoning strategies, workflows, and execution performance
• Collaborate with Data Science and ML teams on evaluation, optimization, and continuous improvement

Skills

• 5–10 years of software engineering experience with strong Python expertise
• 3+ years designing and implementing AI/ML solutions
• Hands-on experience building Generative AI applications using LLMs, RAG, and agentic frameworks (LangChain/LangGraph or equivalent; OpenAI SDK or similar)
• Experience delivering enterprise-grade AI solutions in production
• Experience with distributed onshore/offshore team delivery
• Solid API/service engineering fundamentals (REST/async services, integration patterns)
• Practical understanding of AI observability, evaluation, and production reliability
• Clear written and verbal communication with technical and business stakeholders
• Ability to own delivery end-to-end in a distributed team model
• Pragmatic trade-off judgment between speed, quality, cost, and governance
• Cloud AI platforms (Azure OpenAI, AWS Bedrock, Google Vertex AI, or similar)
• Vector databases / search (pgvector, OpenSearch, Pinecone, Weaviate, or similar)
• LLMOps tooling (tracing, eval harnesses, prompt/version management)
• Enterprise integrations (SharePoint, Confluence, Salesforce)
• Containerized deployment practices (Docker; Kubernetes a plus)
• AI security, PII handling, and guardrail frameworks

Benefits

• Medical, vision, and dental benefits
• 401k retirement plan
• Paid time off
• Paid holidays

Company Overview

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