GenAI Engineer

About the Role

We're seeking a GenAI Agent Engineer to join the ServiceHive team as a core agent builder. This is an individual contributor (IC) role where you’ll take end-to-end ownership of designing, building, and deploying intelligent AI agents that automate customer interactions across platforms like websites, WhatsApp, Instagram, and other digital touchpoints

You’ll play a key role in building AI that helps businesses automate conversations, engage users, and convert leads 24/7.

Your Mission: You'll be the primary hands-on builder of our AI agents - designing conversation flows, training models, implementing agent logic, and continuously improving agent quality. This is a high-ownership role where you'll ship agent features independently.

About ServiceHive: At ServiceHive, we are building the future of AI-powered customer engagement. Our platform enables businesses to automate conversations across multiple channels—helping them capture leads, support customers, and scale operations effortlessly.

We believe in creating practical, high-impact AI solutions that businesses can rely on daily. As part of our team, you’ll work on real-world problems, ship meaningful features, and contribute directly to building a product that transforms how businesses communicate.

Experience Level

2-3 years of professional experience with strong focus on GenAI, LLMs, and AI agent development sand over all 3-5 years of experience in backend development or machine learning.

Key Responsibilities

Core Agent Development (Primary Focus - 80% of time)

• Own the day-to-day development of conversational AI agents - this is your primary responsibility
• Design and implement agent architectures, conversation flows, and decision logic
• Build and fine-tune LLM-based agents using frameworks like LangChain, LlamaIndex, or custom implementations
• Implement prompt engineering strategies to optimize agent responses for different use cases
• Train and fine-tune models for intent recognition, entity extraction, and sentiment analysis
• Build conversation state management, memory systems, and context handling from scratch
• Implement multi-turn conversation logic and dialogue management
• Design and implement agent tools, function calling, and external API integrations
• Optimize agent performance, response quality, and conversation success rates
• Conduct experiments and A/B tests to improve agent effectiveness
• Monitor agent performance and iterate based on conversation analytics

Backend Integration (15% of time)

• Integrate agents with backend APIs and services (with support from Senior Backend Engineer)
• Implement features like lead capture, FAQ responses, and conversation routing
• Write unit tests and integration tests for agent logic
• Debug and fix issues in agent systems

Collaboration & Growth (5% of time)

• Collaborate with the Data Scientist on complex agent challenges and new research directions
• Participate in code reviews and share knowledge with the team
• Stay updated with latest LLMs, agent frameworks, and GenAI research
• Contribute ideas for improving agent capabilities and conversation quality
• Document agent architectures, prompts, and technical decisions

Required Qualifications

Must-Have Skills (Non-Negotiable)

GenAI & Agent Development (Core Requirements)

• LLM Experience: Strong hands-on experience working with LLMs (OpenAI GPT-4, Claude, Gemini, or open-source models)
• Agent Frameworks: Solid experience with at least one agent framework (LangChain, LlamaIndex, AutoGen, CrewAI, Haystack, or similar)
• Prompt Engineering: Proven ability to design, test, and optimize prompts for different use cases
• Conversational AI: Experience building chatbots, conversational agents, or dialogue systems
• Agent Patterns: Understanding of agent architectures (ReAct, Chain-of-Thought, Tool Use, Memory, etc.)
• NLP Fundamentals: Understanding of NLP concepts (intent recognition, entity extraction, sentiment analysis, text classification)

Programming & Development

• Python: Strong proficiency in Python (primary language for agent development)
• LLM APIs: Experience integrating LLM APIs (OpenAI, Anthropic, Cohere, HuggingFace, etc.)
• Backend Basics: Basic understanding of APIs, databases, and backend services
• Version Control: Proficient with Git and collaborative development workflows
• Testing: Experience testing agent outputs, conversation flows, and LLM responses

Proven Track Record

• Portfolio Required: Demonstrable projects or work experience building AI agents, chatbots, or LLM applications
• Examples could include:
  • Production chatbots or conversational AI systems
  • LLM-based applications or tools
  • Personal projects using LangChain, LlamaIndex, or similar frameworks
  • Contributions to open-source agent projects
  • Research or experimentation with agent architectures
• Ability to discuss technical decisions, challenges, and learnings from past agent projects

Soft Skills

• High Ownership: Comfortable taking full ownership of agent development and shipping features independently
• Self-Motivated: Ability to work independently and drive agent improvements without constant direction
• Problem-Solving: Strong analytical thinking and debugging skills for complex agent behaviors
• Communication: Clear communication about agent performance, technical decisions, and blockers
• Experimentation Mindset: Comfortable running experiments, analyzing results, and iterating quickly
• Attention to Detail: Obsessive about conversation quality and user experience
• Curiosity: Passionate about GenAI, LLMs, and staying on top of the latest research

Strong Plus (Highly Desired)

• Fine-tuning Experience: Experience fine-tuning LLMs (GPT-3.5, Llama, Mistral, etc.) for specific use cases
• Multi-Agent Systems: Experience building multi-agent systems or agent orchestration
• RAG Expertise: Strong understanding and implementation of RAG (Retrieval Augmented Generation) patterns
• Vector Databases: Hands-on experience with vector stores (Pinecone, Weaviate, Chroma, Qdrant) for agent memory
• Evaluation Frameworks: Experience with LLM evaluation, conversation quality metrics, and agent testing
• Research Background: Published papers, blog posts, or talks on LLMs, agents, or conversational AI
• Open Source: Contributions to open-source agent frameworks or conversational AI projects
• Production Experience: Experience deploying and monitoring agents in production environments
• Data Analysis: Strong skills with pandas, numpy, and data analysis for conversation analytics

Nice to Have

• Experience with open-source LLMs (Llama, Mistral, Falcon, etc.) and local model deployment
• Knowledge of transformer architectures and how LLMs work under the hood
• Experience with LLM observability tools (LangSmith, Weights & Biases, Helicone, etc.)
• Understanding of MLOps concepts for conversational agents
• Familiarity with cloud platforms (AWS, GCP, Azure) and deployment
• Understanding of async programming in Python (critical for real-time agent execution)
• Experience with streaming responses from LLMs for real-time conversations
• Knowledge of conversation design principles and UX for conversational interfaces
• Understanding of conversational AI safety, content moderation, and compliance
• Experience with webhook handling and real-time message processing
• Familiarity with agile development practices
• Experience with Node.js or TypeScript (for backend integration work)

What Success Looks Like

• First 30 days: Understand our agent architecture and conversation flows; ship your first agent improvement independently
• First 90 days: Take over day-to-day agent development from the Data Scientist; own agent quality and performance metrics; ship multiple agent features independently
• First 6 months: Fully own the agent development roadmap; drive agent improvements based on data and user feedback; mentor the intern on agent development; reduce Data Scientist's IC work by 80%
• First year: Become the go-to expert for agent development; independently design and implement complex agent architectures; contribute to research and strategy discussions; enable the Data Scientist to focus purely on high-level strategy and research

Why Join Us

• Own agent development - This is YOUR domain. You'll have full ownership and autonomy
• High impact from day one - Your agents will power conversations for thousands of users immediately
• Work with an experienced Data Scientist (8 years) who will mentor you initially, then collaborate on strategy
• Rapid growth opportunity - Transition from IC to agent development lead within 6 months
• Be at the forefront of the GenAI and agent revolution in automation
• Small team environment with high visibility and direct impact on product direction
• Work on a product that's loved by creators, startups, and brands across the US & Europe
• See your work impact thousands of users and millions of conversations daily
• Freedom to experiment - Try new agent architectures, LLMs, and techniques
• Competitive compensation with potential for equity

Your Ownership: You'll be the primary owner of all agent development - from conversation design to model selection to deployment. The Data Scientist will guide strategy, but you'll execute and own the day-to-day.