Jobs at AidenAI

Build and deploy scalable AI/ML and LLM applications, including conversational agents, RAG pipelines, vector search systems, agentic workflows, and model-serving APIs. Responsibilities include prompt engineering, model fine-tuning and evaluation, data preprocessing, feature engineering, enterprise integrations, and performance optimization. The role also involves architecture contributions, code reviews, mentoring, documentation, and collaboration with data scientists, product owners, and solution architects.

Leads the design, development, deployment, and governance of generative AI and agentic AI solutions. Manages and mentors AI engineers, develops machine learning models, architects scalable enterprise systems, collaborates with clients and cross-functional teams, and translates business needs into AI strategies. The role also drives innovation through hackathons, presents technical concepts to stakeholders, and ensures AI solutions are ethical, fair, transparent, and production-ready.

Design, develop, and deploy scalable full-stack applications using Python, Django, FastAPI, or Flask; React or Angular; REST and GraphQL APIs; relational and NoSQL databases; Docker, CI/CD, and cloud platforms. Collaborate with AI, cloud, DevOps, and UI/UX teams, while contributing to system design, performance optimization, code reviews, API security, microservices, and AI-enabled modernization initiatives.

Develops, tests, and supports B2B/EDI integrations using SEEBURGER BIS. Responsibilities include creating mappings, configuring trading partners and communication channels, supporting AS2 and SFTP connectivity, testing deployments, monitoring message processing, troubleshooting integration failures, and documenting specifications and test evidence. The role also coordinates with business teams and trading partners on interface requirements.

Designs and leads enterprise AI architectures integrating LLMs, NLP, agentic frameworks, front-end and back-end systems. Responsibilities include selecting AI frameworks, governing data flows and orchestration, integrating APIs and cloud platforms, ensuring security and scalability, and guiding deployment across on-premises, cloud, and hybrid environments. The role also mentors engineering teams and leads AI modernization and automation initiatives.

Manage complex software projects using Agile methodologies, coordinating development teams, product owners, stakeholders, resources, risks, quality, documentation, and delivery. Facilitate Scrum ceremonies, track milestones and project health, remove impediments, improve processes, and ensure technical standards and testing practices are followed. Serve as the primary stakeholder contact while supporting cross-functional collaboration and continuous improvement.

Define and govern scalable, secure, cloud-native full-stack architectures using Python, microservices, modern frontend frameworks, databases, and cloud platforms. Lead architecture reviews, non-functional requirements, DevOps and observability practices, modernization initiatives, technology evaluations, and client solutioning. Mentor engineering teams, collaborate with AI and product stakeholders, and guide delivery of enterprise digital and AI-enabled platforms.

Develop, test, and deploy Python-based AI applications and intelligent agents using LLMs and frameworks such as LangChain. Build RAG, prompt engineering, embedding, knowledge-base, vector database, and data pipeline solutions. Integrate models with enterprise systems through APIs, webhooks, and microservices. Collaborate with data scientists and engineers to optimize models, document solutions, maintain code quality, and support continuous improvement.

Designs and manages secure cloud and hybrid networks across AWS, GCP, and Azure. Responsibilities include virtual networking, routing, VPNs, connectivity, load balancing, firewalls, intrusion prevention, infrastructure automation, troubleshooting, and performance optimization. Collaborates with DevOps, security, and architecture teams while applying network security, compliance, Agile, DevOps, and CI/CD practices.

AI/ML Intern supporting the development, training, evaluation, tuning, deployment, and integration of machine learning, generative AI, and agentic systems. Responsibilities include data preprocessing, experimentation, NLP, computer vision, reinforcement learning, proof-of-concept development, technical documentation, research monitoring, and responsible AI practices. The role involves collaboration with senior engineers and researchers and provides hands-on exposure to enterprise AI applications.

Lead end-to-end delivery of AI and digital transformation initiatives using Agile, Scrum, and agentic development practices. Manage project plans, milestones, risks, dependencies, stakeholder communications, governance, roadmaps, and backlogs. Coordinate AI engineers, data scientists, testers, product owners, architects, and business leaders to deliver solutions on time and within scope. The role may also serve as Product Owner and requires effective use of JIRA, Confluence, Miro, and related delivery tools.

Designs and delivers SEEBURGER BIS B2B/EDI integrations, including mappings, routing, connectivity, trading partner onboarding, SAP integration, monitoring, error recovery, and production support. Leads troubleshooting, release planning, deployment risk assessment, technical estimation, and go-live activities. Reviews integration configurations, mentors developers, maintains technical standards, and communicates delivery status. Requires substantial SEEBURGER BIS expertise, knowledge of X12 or EDIFACT, AS2/SFTP, XML, flat files, and enterprise integration formats.

Manage and enhance enterprise security operations across cloud, infrastructure, applications, endpoints, and networks. Responsibilities include incident response, threat detection, vulnerability management, patching, cloud and network security, IAM, DevSecOps collaboration, application security, endpoint protection, disaster recovery, compliance documentation, and security guidance.

Build and deploy production-grade AI/ML and LLM applications, including conversational agents, RAG pipelines, vector search systems, model-serving APIs, and agentic workflows. Develop and optimize NLP and machine learning models, integrate enterprise platforms, and contribute to scalable AI architecture. Collaborate with cross-functional teams, review code, mentor junior engineers, and establish development standards. The role requires Python, LLM frameworks, vector databases, cloud platforms, containerization, and model evaluation expertise.

Develop and support Python backend services, responsive React or Angular interfaces, REST APIs, microservices, and database integrations. Participate in testing, debugging, code reviews, performance improvements, Git-based collaboration, CI/CD, cloud deployments, and AI-enabled modernization initiatives while working with senior engineering, DevOps, UI/UX, and AI teams.

Lead and mentor full-stack engineers while architecting and delivering cloud-native applications. Develop Python backend services, microservices, secure APIs, and responsive React or Angular interfaces. Design database and third-party integrations, oversee CI/CD deployments, and support scalable, observable systems across cloud platforms. Drive technical decisions, code quality, sprint planning, cross-functional collaboration, and end-to-end delivery for enterprise and AI-enabled solutions.

Design and implement AI-powered client solutions, develop scalable and secure architectures, lead technical scoping, advise customers and development teams, oversee software development, and guide configuration and system integrations across LCNC, BPM, AI, CRM, and full-stack technologies.