Middle AI engineer (AI Agents)
Actively Hiring
Full-time Posted 7 months ago
Responsibilities
- check_circle Design and develop intelligent agents using Python and Autogen framework (or similar frameworks)
- check_circle Collaborate with AI/ML teams to integrate LLMs (e.g., OpenAI, Anthropic, Google, etc.) into multi-agent workflows
- check_circle Develop and maintain cloud-native applications and services (Azure / AWS / GCP)
- check_circle Create robust APIs and integrate them with external/internal systems
- check_circle Implement data ingestion, transformation, and storage logic using SQL, NoSQL, Columnar and Vector DBs
- check_circle Support system scalability, performance, and maintainability using clean architecture principles
- check_circle Contribute to agent orchestration logic, tool integrations, and interaction flows
- check_circle Work closely with the Solutions Architect to align implementation with architecture vision
- check_circle Write tests, maintain documentation, and participate in code reviews
Basic qualifications
- 4+ years of experience in Python development, including async programming
- Hands-on experience with LLM agent frameworks, especially Microsoft Autogen, Langchain, Crew AI, LlamaIndex, etc
- Solid knowledge of cloud platforms: Azure, AWS, or GCP (at least one required)
- Strong understanding of web development and modern API design (e.g., FastAPI, Flask)
- Good experience with SQL and NoSQL databases (e.g., PostgreSQL, MongoDB, Redis)
- Understanding of Machine Learning basics (e.g., inference pipelines, embeddings, vector stores)
- Familiarity with containerized development (Docker) and cloud deployment practices
- Solid Git workflow experience, CI/CD pipelines knowledge
- English proficiency (Intermediate or higher)
Preferred qualifications
- Experience building multi-agent systems or working with agent orchestration tools
- Exposure to RAG (Retrieval-Augmented Generation) and vector search (e.g., FAISS, Weaviate)
- Familiarity with LangChain, LangGraph, LlamaIndex, CrewAI
- Hands-on experience with streaming data, event-driven architecture, or message queues (e.g., Kafka, RabbitMQ and its cloud versions like Kinesis and MQ)
- Knowledge of observability tools (e.g., Prometheus, Grafana, OpenTelemetry)
- Experience with DevOps tools and infrastructure-as-code (Terraform, Helm, etc.)
Tags & Focus Areas
Fulltime Ai Ai Engineer Data Engineer Robotics Generative Ai