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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # AI/ML Engineer - **Company:** Cognizant Technology Solutions Corporation - **Location:** Charleston, WV, United States (Remote available) - **Salary:** $120,000.0 - **Contract:** Permanent contract - **Skills:** LangGraph Framework, AI Evaluation, Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Amazon S3, Cloud Engineering, Cyber Security, Continuous Integration, Data Validation, Monitoring of Systems, Information Extraction, Information Retrieval, Python (Programming Language), PostgreSQL, Machine Learning, Performance Tuning, Search Technologies, Software Engineering, Systems Integration, Performance Testing, Retrieval-Augmented Generation, Large Language Models, Prompt Engineering, Generative AI, AWS Lambda, Backend, Git, Fastapi, Amazon Relational Database Service, Pgvector, AI Platforms, Integration Tests, Information Technology, Restful APIs, Document Classification, Intelligent Document Processing, Docker, Microservices - **Published:** October 15, 2026 - **Apply:** https://dejobs.org/x/x/916943FB4D6E42B3BBF68F812AE501E9/job/ ## About the Role * Bachelor's degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Engineering, or a related field, or equivalent work experience. * Strong hands-on experience developing enterprise AI/ML solutions. * Expertise in: * Python * FastAPI * REST APIs * Experience with: * Large Language Models (LLMs) * Generative AI * Prompt Engineering * Retrieval-Augmented Generation (RAG) * Strong understanding of: * Embeddings * Vector Databases * Semantic Search * Knowledge Retrieval * Hands-on experience with: * AWS Bedrock * AWS Lambda * Amazon S3 * Amazon RDS * Experience with: * PostgreSQL * Git * Docker * CI/CD Pipelines * Strong analytical, troubleshooting, and problem-solving skills. * Excellent communication and collaboration skills., * Experience building enterprise Generative AI applications. * Experience with Intelligent Document Processing (IDP) platforms and solutions. * Expertise in: * Semantic Search * Knowledge Retrieval Systems * AI Authoring Applications * Experience with: * LangChain * LangGraph * Knowledge of: * AI Evaluation Frameworks * Model Monitoring * AI Governance * Experience working within Life Sciences, Pharmaceutical, Healthcare, or other regulated industries. * Familiarity with cloud-native architecture patterns and microservices frameworks. Technical Expertise Candidates should demonstrate experience in: * Generative AI * Large Language Models (LLMs) * Prompt Engineering * Retrieval-Augmented Generation (RAG) * Intelligent Document Processing (IDP) * Semantic Search * Knowledge Retrieval * Vector Databases * PGVector * Python Development * FastAPI * REST API Design * AWS Bedrock * AWS Lambda * Amazon S3 * Amazon RDS * PostgreSQL * Docker * Git * CI/CD * LangChain * LangGraph * Responsible AI ## Description This is a hands-on engineering role focused on designing, developing, deploying, and supporting AI-powered Document Processing and AI Authoring platforms. The ideal candidate will have deep experience building enterprise Generative AI solutions leveraging Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), Intelligent Document Processing (IDP), semantic search, and cloud-native AI architectures. You will collaborate with Solution Architects, Product Owners, Data Engineers, and Software Engineering teams to develop innovative AI solutions that deliver scalable, secure, and production-ready business capabilities. Work Model This position is fully remote within the United States , providing the flexibility to work from home while collaborating with global teams across multiple locations and time zones. Occasional travel may be required for client meetings, planning sessions, team collaboration events, and strategic initiatives., Generative AI Solution Development * Design, develop, and deploy enterprise-grade Generative AI solutions using Large Language Models (LLMs). * Build intelligent document processing and AI-assisted authoring capabilities supporting complex business workflows. * Develop and optimize Retrieval-Augmented Generation (RAG) architectures to enhance response quality and contextual relevance. * Implement prompt engineering strategies to improve AI performance, accuracy, and reliability. * Collaborate with product and business teams to translate business requirements into scalable AI solutions. Document Intelligence & AI Authoring * Develop capabilities for: * Document Extraction * Document Classification * Data Validation * Knowledge Retrieval * AI-Assisted Content Generation * Design workflows that automate document understanding and content creation processes. * Build validation frameworks that improve accuracy, traceability, and confidence in AI-generated outputs. * Support end-to-end lifecycle management of AI-powered document solutions. RAG, Semantic Search & Knowledge Retrieval * Design and optimize embedding strategies for enterprise data and document repositories. * Implement vector search and semantic retrieval solutions to improve knowledge discovery. * Develop grounding mechanisms that ensure AI responses are accurate, contextual, and explainable. * Build and maintain retrieval pipelines that support high-performance search and content generation capabilities. * Evaluate and enhance retrieval effectiveness using modern AI evaluation methodologies. API & Microservices Development * Design and develop APIs and microservices using Python and FastAPI. * Build scalable and reusable backend services supporting AI applications and integrations. * Develop secure API-based connections between AI platforms, enterprise systems, and third-party applications. * Support microservice deployment, monitoring, maintenance, and optimization activities. AWS Cloud & AI Platform Engineering * Build and support cloud-native AI solutions leveraging AWS services. * Develop and maintain solutions utilizing: * AWS Bedrock * AWS Lambda * Amazon S3 * Amazon RDS * Related AWS services * Implement scalable, secure, and highly available cloud architectures for AI workloads. * Collaborate with cloud and platform engineering teams to optimize performance and cost efficiency. Testing, Quality & Operational Excellence * Create and execute: * Functional Tests * Integration Tests * AI Quality Validation Tests * Performance Tests * Support AI model evaluation, monitoring, and continuous improvement initiatives. * Troubleshoot production issues and implement long-term solutions. * Contribute to quality engineering processes and automated testing frameworks. Security, Governance & Responsible AI * Ensure solutions comply with enterprise security standards and governance requirements. * Implement Responsible AI practices that support explainability, transparency, and risk management. * Adhere to data privacy, compliance, and information security requirements. * Support auditability, monitoring, and governance controls within AI platforms.