Job Title: Front End Engineer - AI
Location: Remote / Onsite / Hybrid
Job Type: Full-time / Contract
About the Role
We are seeking an experienced and forward-thinking Front End Engineer to join our AI Ops delivery team. In this role, you will lead the design, development, and delivery of GenAI-powered agentic systems that automate complex business processes across domains such as HR, Payroll, SAP, and Client Delivery. You’ll serve as the technical lead within a Business/Core Pod, responsible for mentoring engineers, shaping solution architectures, and ensuring delivery excellence. This role combines deep hands-on engineering with architectural ownership and close collaboration with product managers and domain experts.
Key Responsibilities
- Own the technical design and architecture of GenAI agent workflows within the Business Pod
- Serve as a hands-on developer, actively contributing to the design, coding, and delivery of production-grade agents and tools
- Lead code and design reviews, ensuring alignment with architectural standards and platform best practices
- Collaborate with the AI Ops Core Pod to adopt and influence reusable assets, frameworks, and integration patterns
- Design and implement LLM-powered agent workflows using frameworks such as LangChain, LangGraph, or CrewAI
- Develop prompt chains, agent tools, and custom modules that support reasoning, summarization, and multi-step task execution
- Integrate agents with enterprise systems (e.g., Workday, SAP, Salesforce) via REST APIs, SDKs, or message queues
- Build and manage agent lifecycle components, including initialization, memory/state handling, and fallback logic
- Implement and consume vector store integrations, prompt templates, and retrieval-augmented generation (RAG) techniques
- Ensure workflows are robust, secure, and observable, with proper logging, monitoring, and exception handling
- Partner with Product Managers, SMEs, and QA to translate business processes into agentic workflows and iterate based on feedback
- Contribute to the automation of testing, deployment, and validation pipelines for AI agents
- Maintain thorough documentation of agent behavior, design decisions, and integration logic for operational readiness and knowledge transfer
Preferred Qualifications
- Bachelor’s or Master’s degree in Computer Science, Software Engineering, or a related technical field
- 4+ years of experience in developing and deploying production-grade AI/ML or automation solutions
- Strong proficiency in Python, with hands-on experience using FastAPI, REST APIs, and background task orchestration
- Deep familiarity with agentic frameworks such as LangChain, LangGraph, CrewAI, ReAct, or similar
- Understanding of LLM orchestration, including prompt design, tool usage, context management, and agent memory
- Experience integrating with enterprise systems via APIs, event/message queues (e.g., Kafka, Service Bus), and webhooks
- Solid foundation in distributed system design, including state management, retries, error handling, and resilience
- Experience with business process automation or workflow automation in real-world environments
- Comfortable working with SQL/NoSQL databases, including data modeling and validation
- Knowledge of containerization (Docker), orchestration platforms (Kubernetes), and deployment to cloud platforms (Azure, AWS, or GCP)
- Understanding of security concepts including authentication (OAuth2), authorization, and secure API integration
- Exposure to multi-agent patterns (e.g., supervisor-worker, planner-executor, judge-critic) is a strong plus
Nice to Have
- Contributions to open-source agent frameworks or AI tooling.
- Experience working with observability and monitoring tools to track agent performance.
- Exposure to knowledge graphs, memory management systems, or retrieval-augmented generation (RAG) pipelines.