AI Engineer

Key Responsibilities

• API Development: Build robust APIs and microservices using FastAPI to integrate AI models into scalable applications.
• Data Management: Work with vector databases to store and retrieve structured and unstructured data for GenAI workflows.
• Prompt Engineering: Design and optimize prompts using techniques like Chain of Thought (CoT) and advanced prompting strategies to improve model outputs.
• Agent Development: Develop autonomous and function-calling agents using frameworks like LangChain, LlamaIndex, or custom logic.
• Collaboration: Work closely with cross-functional teams including UI, DevOps, and product stakeholders to define goals and deliver solutions.
• Documentation: Maintain comprehensive documentation for agents, prompts, APIs, workflows, and architecture to support both technical and non-technical audiences.
• Frameworks & Tools: FastAPI, LangChain, LlamaIndex, GitHub Actions, Terraform.
• Generative AI Techniques: Retrieval-Augmented Generation (RAG), Function Calling Agents, Prompt Engineering.
• Cloud Platforms: AWS, Google Cloud Platform (Vertex AI, GKE), Microsoft Azure.

Must-Have Skills

• 1–3 years of experience in backend development using Python, with a strong foundation in building RESTful APIs using FastAPI.
• Solid understanding of Generative AI concepts, including prompt engineering, RAG, and agent workflows (e.g., using LangChain or LlamaIndex).
• Hands-on experience working with vector databases (e.g., Milvus, Pinecone, or Vertex AI Vector Search).
• Proficient in building LLM function-calling agents and integrating them into production-ready pipelines.
• Experience deploying and managing applications on cloud platforms such as GCP, AWS, or Azure.
• Familiarity with CI/CD pipelines, and version control (Git/GitHub)
• Strong problem-solving mindset and ability to work in fast-paced, iterative development environments.
• Excellent written and verbal communication skills for cross-functional collaboration and documentation.

Nice-to-Have Skills

• Knowledge of multi-agent orchestration, tools like CrewAI, AutoGen, LangGraph, or task-driven LLM workflows.
• Exposure to frontend integration with React-based UIs or familiarity with building full-stack features.
• Prior experience in enterprise-grade automation tools or telecom-specific AI solutions.
• Understanding of data governance, security best practices, and access control (e.g., RBAC in AI workflows).