AI Engineer

We are looking for a driven AI Engineer to join our engineering team. In this role, you will focus on building, testing, and deploying autonomous AI agents and multi-agent systems. You will bridge the gap between traditional software engineering and modern Generative AI, working to enable LLMs (Large Language Models) to interact with external tools, APIs, and data sources. To adhere to our corporate location policies, this resource will be required to be local to the surrounding Atlanta, GA . You are required to adhere to our Return To Office (RTO) / weekly onsite requirements (Tuesday, Wednesday, and Thursday). What you'll do

• Agent Development & Testing: Perform development activities focused on AI Agents, including designing prompt chains, implementing tool-calling logic (function calling), and conducting unit tests for stochastic AI outputs. Work on projects involving RAG (Retrieval-Augmented Generation) and contribution to agent frameworks.

• Performance Optimization: Participate in the estimation process for AI features. Diagnose and resolve specific AI performance issues, such as latency in LLM responses, token usage optimization, and reducing hallucination rates in agentic workflows.

• Documentation & Knowledge Sharing: Document agent architectures, prompt templates, and "chains of thought" so that other developers can understand and iterate on the AI logic with minimal effort.

• Full-Stack AI Integration: Develop and operate scalable AI applications from the backend logic (Python/LangChain) to the API layer, focusing on security (Guardrails) and operational excellence. Ensure agents can reliably execute tasks in a production environment.

• Modern AI Practices: Apply modern software and AI engineering practices, including LLMOps, evaluation pipelines (Evals), vector database management, and standard CI/CD/Infrastructure-as-code.

• System Integration: Work across teams to integrate AI Agents with existing internal systems, and third-party APIs to enable agents to perform "actions" rather than just generating text.

• Innovation & Agile: Participate in technology roadmap discussions to turn business requirements into functional autonomous agent solutions. Collaborate within a tight-knit engineering team employing agile practices.

• Debugging & Triage: Triage product issues: Debug, track, and resolve issues by analyzing traces (e.g., LangSmith, Arize) to understand the root cause of agent failures or loop errors.

• Implementation: Lead efforts for Sprint deliverables and solve problems of medium complexity regarding context management and memory., Role Overview This role involves contributing to an AI evaluation program focused on advanced silicon and chip-design workflows. We are looking for senior digital chip design and v…

• 1 day ago
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• Bachelor's degree or equivalent experience

• 5-7 years of IT engineering experience

• 5+ years strong programming proficiency in Python with the ability to process, clean, and structure messy, unstructured data at scale.

• 1+ year experience in architecting and building autonomous Agentic Workflows and multi-agent orchestration using frameworks like preferably with Google ADK/A2A.

• 1+ year of hands-on experience in building and scaling Vector Search infrastructure and embedding pipelines using databases like FAISS, PGVector.

• 2+ years of experience in engineering production-grade RAG (Retrieval-Augmented Generation) pipelines, requiring hands-on experience with advanced retrieval strategies (semantic, lexical, hybrid, weighted hybrid, advanced reranking techniques using ML libraries, context distillation, compression), semantic/document-aware chunking, metadata extraction, and hybrid search.

• 1+ year of direct experience in Agentic AI development, moving beyond basic prompt engineering to design and deploy autonomous, multi-agent systems that leverage tool-calling, memory, and complex reasoning loops (e.g., ReAct, Plan-and-Solve).

• At least one year of experience deploying, testing, and scaling AI backend services in cloud environments (GCP) using containerization (Docker, Kubernetes).

• At least one year of experience implementing AI security and guardrails, including mitigating prompt injection, hallucination reduction, and ensuring data privacy in enterprise applications.

• At least one year of experience researching, prototyping, and proposing novel AI architectures to solve ambiguous problems, rather than relying on predefined technical specs.

What could set you apart

• Experience with Google Cloud Platform services, preferably dataflow, bigquery, spanner, Vertex AI, cloudrun

• Experience in JAVA, with the ability to process, clean, and structure messy, unstructured data at scale.

• Working knowledge of Graph Database * Experience in designing A2A (Application-to-Application) integration frameworks and managing complex MCP (Multi-Cloud Platform) architectures.

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