Top Tech Jobs & Startup Jobs

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.