AI Engineer AI Agent Development

AI Engineer AI Agent Development

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About The Role

Key Responsibilities

AI Agent Development

• Design, develop, and deploy production-grade AI Agents using Google Vertex AI Agents or AWS Bedrock Agents.
• Build end-to-end AI agent workflows including LLM orchestration, tool invocation, guardrails, and monitoring.
• Implement RAG (Retrieval-Augmented Generation) pipelines for dynamic and context-aware agent responses.
• Integrate AI Agents with enterprise applications, APIs, data sources, and external tools.
  

AI Engineering & Architecture

• Build scalable backend components using Python, FastAPI/Flask, and microservice patterns.
• Develop and integrate RESTful APIs and secure agentsystem interactions.
• Implement model evaluation, prompt engineering, embeddings, hallucination controls, and response validation.
• Work across Vector Databases (Pinecone, ChromaDB, Milvus, Weaviate) and RDBMS (PostgreSQL, MySQL).
  

LLM & Platform Engineering

• Fine-tune, optimize, and evaluate LLMs for accuracy, safety, and performance.
• Leverage Google Vertex AI (Model Garden, Vector Search, Agents, Workbench) or AWS Bedrock (Agents, Guardrails, Knowledge Bases).
• Collaborate with product teams to understand use cases and convert them into AI agent workflows.
  

Quality, Monitoring & MLOps

• Deploy AI agents to production using CI/CD, containerization (Docker), and cloud deployment best practices.
• Implement logging, monitoring, evaluation pipelines, and feedback loops to ensure reliability.
• Apply security, privacy, and responsible AI principles (PII handling, content filters, guardrails).
  

Required Skills & Experience

• 1 to 3 years of hands-on AI Agent development experience.
• Successfully developed and deployed at least one AI Agent into production.
• Hands-on experience with Google Vertex AI, AWS Bedrock, LLMs, and RAG pipelines.
• Strong experience with Vector Databases (Pinecone, Weaviate, Chroma, Milvus) and RDBMS.
• Proficiency in Python for backend and AI development.
• Experience with API development (FastAPI, Flask, API Gateway, Cloud Functions, Lambda).
• Familiarity with prompt engineering, embeddings, and LLM evaluation.
• Strong understanding of cloud platforms (Google Cloud or AWS).
• Experience handling unstructured data (documents, PDFs, images, JSON, logs).
  

Nice-to-Have Skills

• Experience with Google AI Studio, Vertex AI Search & Conversation, or Vertex AI Extensions.
• Experience with LangChain, LlamaIndex, CrewAI, AutoGen, or similar AI orchestration frameworks.
• Exposure to MLOps tools (Vertex Pipelines, AWS SageMaker, MLflow).
• Knowledge of GenAI safety, guardrails, PII redaction, and content filters.
• Experience building multi-agent systems.
• Understanding of event-driven architectures (Pub/Sub, SQS, EventBridge).
• Familiarity with frontend frameworks (React) to support simple agent UI prototypes.
  

Soft Skills & Work Style

• Ability to work in a fast-paced, startup-like environment.
• Strong problem-solving mindset with ownership of end-to-end delivery.
• Excellent communication and documentation skills.
• Ability to collaborate cross-functionally with product managers, designers, and engineering leaders.
• Passion for innovation, experimentation, and pushing the boundaries of applied AI.
  

Why Join Us?

• Work on cutting-edge applied AI technologies.
• Opportunity to build real-world AI agents used by global enterprises.
• Fast-paced environment with room for rapid growth.
• Be part of an AI Innovation Lab shaping the future of life sciences and enterprise AI.