Artificial Intelligence Engineer

Job Title: AI Engineer Generative AI, RAG & Agentic AI

Experience: 612 Years

Remote

Key Roles and Responsibilities

• Design, develop, and deploy enterprise-grade Generative AI applications using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and Agentic AI architectures.
• Build end-to-end RAG pipelines, including document ingestion, parsing, preprocessing, chunking, embedding generation, vector indexing, retrieval, reranking, and context-grounded response generation.
• Develop automated document-processing pipelines to extract and normalize information from PDFs, Word documents, Excel workbooks, scanned files, tables, forms, and other structured and unstructured data sources.
• Implement OCR, layout detection, table extraction, schema mapping, and data validation to handle heterogeneous document formats, complex tables, merged cells, multi-level headers, and inconsistent data structures.
• Design and optimize vector search, keyword search, hybrid retrieval, metadata filtering, query rewriting, semantic search, and reranking strategies to improve retrieval accuracy and relevance.
• Develop prompt engineering strategies, structured outputs, function calling, and context-management techniques to improve LLM response quality, consistency, and reliability.
• Evaluate and select appropriate LLMs and embedding models based on accuracy, reasoning capabilities, context-window size, latency, throughput, cost, security, and deployment requirements.
• Implement LLM application tracing, evaluation, and observability using LangSmith or equivalent tools to monitor prompts, model calls, retrieval operations, tool executions, latency, token usage, and errors.
• Use MLflow or equivalent platforms for experiment tracking, evaluation management, configuration comparison, artifact versioning, and reproducible AI development workflows.
• Develop evaluation datasets and automated testing pipelines to measure answer correctness, faithfulness, contextual relevance, retrieval performance, citation accuracy, and structured-output validity.
• Identify and mitigate LLM hallucinations through evidence-grounded generation, source validation, citation verification, confidence-based controls, deterministic validation, and abstention mechanisms.
• Design and implement Agentic AI workflows using LangGraph, LangChain, LlamaIndex, or similar frameworks to integrate LLMs with APIs, databases, enterprise tools, and business processes.
• Implement agent orchestration, workflow routing, state management, tool calling, retries, error handling, and human-in-the-loop approval mechanisms where required.
• Develop scalable backend services and AI APIs using Python, FastAPI, REST APIs, and related technologies to integrate AI capabilities into enterprise applications.
• Optimize AI application performance, including retrieval quality, inference latency, token consumption, throughput, caching, concurrency, and overall operational cost.
• Deploy and maintain AI applications on cloud platforms such as Microsoft Azure, AWS, or Google Cloud, using Docker, Kubernetes, CI/CD pipelines, and appropriate deployment practices.
• Implement security controls, authentication, authorization, data privacy, prompt injection protection, sensitive-data handling, audit logging, and responsible AI practices.
• Monitor production AI applications, investigate quality degradation and failures, perform root-cause analysis, and implement continuous improvement through evaluation results and user feedback.
• Collaborate with data engineers, ML engineers, architects, DevOps teams, and business stakeholders to translate requirements into scalable, secure, and reliable AI solutions.
• Maintain technical documentation, automated tests, reusable components, version control, and engineering best practices throughout the AI application lifecycle.

Requirements

• 6–12 years of overall experience in software engineering, AI/ML engineering, data engineering, or related technical domains, with hands-on experience developing AI-powered applications.
• Strong programming skills in Python, including application development, API integration, data processing, debugging, testing, and exception handling.
• Hands-on experience building and deploying Generative AI applications using LLM APIs or open-source language models.
• Strong understanding of RAG architecture, document ingestion, text preprocessing, chunking strategies, embeddings, vector databases, retrieval optimization, and grounded response generation.
• Practical experience with LLM orchestration frameworks such as LangChain, LangGraph, or LlamaIndex.
• Experience with vector databases and search technologies such as Azure AI Search, Pinecone, Qdrant, Weaviate, Chroma, FAISS, Elasticsearch, or OpenSearch.
• Hands-on experience with prompt engineering, structured output generation, function calling, context management, and LLM integration.
• Experience processing structured and unstructured documents, including PDFs, Excel files, Word documents, scanned documents, and complex tables, using document parsers, OCR, and document intelligence technologies.
• Knowledge of table extraction, schema normalization, metadata management, data quality validation, and document versioning.
• Practical experience with LangSmith, MLflow, or equivalent platforms for LLM tracing, experiment tracking, evaluation, observability, and performance optimization.
• Strong understanding of LLM evaluation methodologies, including retrieval precision and recall, answer correctness, faithfulness, contextual relevance, citation validation, and regression testing.
• Experience implementing hallucination mitigation, evidence validation, confidence thresholds, fallback mechanisms, and safe abstention strategies.
• Understanding of LLM and embedding model selection based on accuracy, latency, cost, context length, multilingual capabilities, privacy, and deployment constraints.
• Experience integrating AI applications with REST APIs, SQL/NoSQL databases, enterprise systems, and external tools.
• Knowledge of Agentic AI concepts, tool calling, workflow orchestration, state management, and multi-step LLM execution.
• Familiarity with cloud AI platforms such as Azure OpenAI, Azure AI Foundry, Amazon Bedrock, or Google Vertex AI.
• Experience with Git, Docker, CI/CD, automated testing, application logging, monitoring, and production deployment practices.
• Understanding of AI security, access control, data privacy, prompt injection risks, sensitive-data protection, and responsible AI principles.
• Strong analytical, problem-solving, debugging, communication, and collaboration skills, with the ability to deliver production-ready AI solutions.