MUST-HAVE SKILLS

• 6+ years in software/AI development, including 2+ years building LLM-driven applications.
• Strong Python skills with experience in reusable agent stacks or RAG pipelines.
• Hands-on with LLM APIs (OpenAI, Anthropic, HuggingFace, etc.) and building prompt/tool workflows.
• Experience integrating vector databases (Weaviate, FAISS, pgvector, Pinecone).
• Familiarity with multi-agent orchestration (CrewAI, AutoGen, LangGraph, or custom).
• Experience shipping AI tools or services into production with containerization and cloud platforms (Docker, Kubernetes, GCP/AWS).
• Understanding of agent design (memory management, context management, tool contracts).
• Knowledge of Model Context Protocol (MCP) or similar standards.

BONUS SKILLS

• Experience with agent evaluation, prompt testing, and reducing hallucinations/latency.
• Exposure to hybrid workflows combining LLMs with computer vision.
• Knowledge of event-driven or streaming agents (Kafka, LCEL, reactive frameworks).
• Experience building simple interfaces with Streamlit, Gradio, or browser automation.
• Familiarity with fine-tuning techniques (LoRA/QLoRA) or multi-modal agent training.

ABOUT THE ROLE

We’re looking for a hands-on GenAI Developer to design and productionize intelligent agentic systems powered by LLMs, computer vision, and vector databases. You’ll build autonomous agents that reason, retrieve, and act - driving real-world GenAI applications from prototype to production.
This is a true builder role for someone who enjoys taking ideas from notebooks to live users. You’ll work closely with Software and AI Engineers to integrate workflows into production and shape how our agents interact with tools, memory, data, and humans.

KEY RESPONSIBILITIES

• Design and improve LLM-powered agents for task planning, execution, retrieval, and memory.
• Build modular agent stacks using frameworks like LangChain, CrewAI, AutoGen, or custom orchestrators.
• Develop workflows that connect agents to APIs, computer vision models, and vector databases.
• Lead prompt engineering, RAG pipelines, and multi-agent coordination strategies.
• Collaborate with engineers to productionize agents with proper observability and debuggability.
• Evaluate agent behavior through logging, telemetry, and custom evaluation tools.
• Contribute to architecture patterns such as task workflows, tool interfaces, memory optimization, and feedback handling.
• Work with infrastructure teams to deploy agentic systems to production, ensuring monitoring, scaling, and rollback safety.
• Ensure compliance with Model Context Protocol (MCP) standards for structured context and execution.