AI Engineer

AI Engineer

Key Responsibilities:

Software Engineering & System Design

• Write, test, and deploy production-quality code with a strong focus on scalability and performance.

• Design and implement end-to-end systems including API, database, and application layers.

• Consider memory usage, performance optimization, and modular architecture in code design.

• Follow modern DevOps practices for deployment and environment management.

Python Development

• Write clean, modular Python code with proper environment management (virtualenv, poetry, conda).

• Build and deploy RESTful APIs using frameworks like FastAPI or Flask.

• Connect applications to relational and NoSQL databases.

• Containerize applications using Docker and deploy in Kubernetes / cloud environments.

Database Skills

• Write and optimize SQL and PL/SQL queries for data retrieval and transformation.

• Integrate database logic effectively into application workflows.

Generative AI Development

• Understand how Large Language Models (LLMs) work — architecture, limitations, and tuning methods.

• Design and implement RAG (Retrieval-Augmented Generation) systems:

• Build pipelines connecting vector databases, LLMs, and application layers.

• Optimize retrieval and response accuracy through prompt engineering and context management.

• Apply best practices and advanced RAG techniques (chunking strategies, caching, query re-ranking).

• Develop and manage prompt templates, parameter configurations, and evaluation metrics.

Agentic AI Systems

• Understand AI agents, their lifecycle, and how they integrate with tools and APIs.

• Implement tool calling and human-in-the-loop workflows.

• Design multi-agent architectures using frameworks such as LangChain or LangGraph.

• Apply agent orchestration best practices, ensuring reliability, observability, and control.

Required Skills & Qualifications

• Strong understanding of software design principles and ability to think like a developer (language agnostic).

• Proficiency in Python with good exposure to building APIs, database integration, and container-based deployment.

• Working knowledge of SQL / PL-SQL for querying and joining data.

• Experience in developing or integrating GenAI or RAG-based systems using OpenAI, Anthropic, or similar models.

• Familiarity with LangChain, LangGraph, or similar frameworks for AI workflow orchestration.

• Strong debugging, optimization, and analytical skills.

• Excellent communication and collaboration abilities in a fast-paced environment.

Nice to Have

• Exposure to cloud platforms (AWS, GCP, Azure) for AI workload deployment.

• Experience with vector databases (FAISS, Pinecone, Weaviate, or Chroma).

• Knowledge of monitoring and observability tools for AI systems.

• Familiarity with CI/CD pipelines, GitOps, and infrastructure automation.