Senior Artificial Intelligence Engineer

Role & Responsibilities

• Design, develop, and deploy production-grade AI and Generative AI solutions using Python, LLMs, NLP, and Retrieval-Augmented Generation (RAG).
• Build scalable retrieval architectures using vector search, hybrid retrieval, and other information retrieval techniques.
• Develop and optimize RAG pipelines using technologies such as OpenSearch and vector databases.
• Implement structured prompt engineering and LLM-based solutions to improve response quality, relevance, and reduce hallucinations.
• Design automated evaluation approaches to benchmark LLM output quality and retrieval performance.
• Develop reliable AI services and APIs using Python, FastAPI, Docker, and modern software engineering practices.
• Write clean, maintainable, typed and testable Python code with appropriate unit testing and code-quality practices.
• Follow SDLC best practices including Git workflows, code reviews, CI/CD, testing, and production deployment.
• Work closely with Data Scientists, Software Engineers, and business stakeholders to convert AI experiments into reliable production solutions.
• Continuously evaluate emerging GenAI technologies and apply practical solutions that deliver measurable business value.

Preferred Candidate Profile

• 5+ years of experience in software engineering with strong experience in AI, NLP, Information Retrieval, or Generative AI.
• Strong hands-on expertise in Python and experience developing production-grade software applications.
• Proven experience building and deploying LLM and RAG-based applications in enterprise environments.
• Strong understanding of NLP, LLMs, Prompt Engineering, embeddings, vector search, and retrieval architectures.
• Hands-on experience with RAG, hybrid retrieval, vector databases/search platforms, and preferably GraphRAG.
• Experience with OpenSearch or similar vector/search technologies is highly desirable.
• Experience with FastAPI, Docker, Git, CI/CD, pytest, and modern Python development practices.
• Experience with AI evaluation methodologies or automated pipelines for measuring LLM and retrieval quality.
• Exposure to AWS Bedrock, Azure OpenAI, or similar cloud-based AI platforms is an advantage.
• Knowledge of MCP (Model Context Protocol) and modern API standards is a plus.
• Strong analytical and logical reasoning skills with the ability to solve complex AI problems.
• Strong communication skills with the ability to work effectively with both technical and non-technical stakeholders.
• Hands-on, adaptable, pragmatic, and ownership-driven approach to solving business problems.