AI Engineer – Generative AI & Agentic AI

This is a remote position.

Role Overview

The AI Engineer designs, develops, integrates, tests, and operates production-ready AI applications, with a focus on Generative AI (GenAI), Retrieval-Augmented Generation (RAG), and agentic workflows. The role turns product and business requirements into reliable software using foundation models, enterprise data, APIs, and cloud services. The AI Engineer works closely with AI Architects, software engineers, data engineers, product teams, and security stakeholders to deliver useful, secure, observable, and cost-aware AI capabilities such as conversational assistants, document intelligence, summarization, and knowledge Q&A.

Key Responsibilities

· Develop maintainable, tested Python services and components for GenAI applications, data processing, retrieval, and model integration.

· Implement prompt templates, structured outputs, tool/function calling, and model-provider integrations; evaluate and iterate based on task requirements.

· Build end-to-end RAG pipelines including document ingestion, parsing, cleaning, metadata extraction, chunking, embedding, indexing, retrieval, reranking, and context construction.

· Integrate vector and hybrid search systems; tune retrieval and grounding quality using relevant evaluation datasets and metrics.

· Develop agentic workflows with bounded tool use, state management, routing, retries, error handling, and human approval where needed.

· Integrate enterprise APIs, databases, document repositories, and business systems while respecting authentication, permissions, and data boundaries.

· Build application APIs and user interfaces or prototypes using frameworks such as FastAPI and Streamlit; collaborate with frontend engineers for production interfaces.

· Deploy and operate AI services in cloud environments; use containers, CI/CD, configuration management, and environment separation.

· Instrument applications with logs, traces, metrics, and feedback capture; monitor quality, latency, token usage, failures, and cost.

· Write unit, integration, retrieval, evaluation, and regression tests; participate in code reviews, documentation, and technical knowledge sharing.

· Apply security and responsible AI practices, including prompt-injection defenses, sensitive-data handling, output validation, and safe tool permissions.

Requirements

Mandatory Skills & Experience

1. Python & Software Engineering

· Strong practical Python skills, including writing modular, readable, maintainable, and testable code.

· Experience with common Python libraries such as NumPy, pandas, and Pydantic; familiarity with typing, packaging, virtual environments, and dependency management.

· Working knowledge of REST APIs, JSON, asynchronous programming (async/await), concurrency, exception handling, and API integration.

· Familiarity with Git, code reviews, unit testing (e.g., pytest), debugging, and basic software design principles.

1. Generative AI & LLM Application Development

· Hands-on experience building LLM-powered applications such as chat, summarization, extraction, document assistants, or question-answering systems.

· Understanding of tokens, context windows, prompt design, system instructions, temperature and other generation parameters, structured generation, and model limitations.

· Experience integrating hosted or open-source LLMs through APIs or model-serving endpoints.

· Ability to use streaming, caching, batching where appropriate, and asynchronous processing to improve responsiveness and manage costs.

1. RAG, Embeddings & Search

· Practical experience implementing RAG: document loading, parsing, chunking, embedding generation, vector indexing, retrieval, context assembly, and grounded response generation.

· Understanding of dense retrieval, keyword search (BM25), hybrid search, metadata filtering, query rewriting, and reranking.

· Familiarity with vector/search technologies such as FAISS, pgvector, Pinecone, Qdrant, Weaviate, Chroma, Milvus, Elasticsearch/OpenSearch, or cloud equivalents.

· Ability to evaluate retrieval and answer quality using test questions, relevance/groundedness checks, and feedback.

1. AI Frameworks & Agentic Workflows

· Experience with at least one current LLM application framework, such as LangChain or LangGraph; familiarity with framework abstractions and trade-offs.

· Experience or working familiarity with agentic workflow frameworks such as Microsoft Agent Framework / Auto…