S7works

(Python/Golang, Microservices, Kubernetes) The Software Engineering team delivers next-generation application enhancements and new products for a changing world. Working at the cutting edge, we design and develop software for platforms, peripherals, applications and diagnostics - all with the most advanced technologies, tools, software engineering methodologies Designing and writing test automations in a Test-driven Development environment using test automation frameworks in CI/CD/CT framework: Integration, Regressions, UAT, Performance testing etc investigating problems in software by executing automations Ability to tune test frameworks and CI/CD pipelines Demonstrate the ability to work and flourish in fast paced agile development environments Take the first step towards your dream career. Every S7works team member brings something unique to the table. Heres what we are looking for with this role: Essential Requirements Experience in automations for microservices based cloud apps in a CI/CD/CT environments High degree of competence in Python3, Golang, Java Experience in using Robot or Pytest frameworks to perform API based testing Container environments like Docker, Kubernetes, including code packaging, use of built-in test tools or language-specific packages to test test-driven development, CI/CD process, continuous testing, agile development with Jira/Github/Jenkins/Collaborator or similar platforms Possesses strong debugging, triaging and troubleshooting skills Performance tuning by identifying bottle necks and working with developers to solve them Willingness and programming skills to double up as developers Collaborate with Documentation teams in writing internal and customer facing publications Roles and Responsibilities Desired Candidate Profile Perks and Benefits

Thorough understanding of React .js and its core principles / (Ract.js or React Native) Experience with popular React .js workflows (such as Flux or Redux) Experience with Flutter Familiarity with newer specifications of EcmaScript Experience with data structure libraries (e.g., Immutable.js) Knowledge of isomorphic React is a plus Familiarity with RESTful APIs Knowledge of modern authorization mechanisms, such as JSON Web Token Experience with SPA (single page app) implementation, responsive web design implementation, microservices, and API based flexible backend construction Experience building any of these features: Personalization, Complex user mgmt. and security requirements, Secure document mgmt. (upload, download), Online collaboration tools, Alerts, notifications, Chatbot / live chat/video chat integration. Roles and Responsibilities Desired Candidate Profile Perks and Benefits

Job Title: AI Engineer Agent Development Location: [On-site / Remote / Hybrid] Employment Type: [Full-time / Contract] About the Role We are looking for an AI Engineer specialized in agent development to design, build, and scale intelligent autonomous systems. This role will focus on LLM-powered agents, multi-step reasoning, tool integrations, and orchestration frameworks that allow AI systems to interact with APIs, data sources, and humans. You will be at the forefront of shaping how generative AI is applied in production — developing robust, safe, and scalable agentic architectures. Key Responsibilities Design, develop, and deploy AI agents capable of autonomous reasoning, planning, and multi-step task execution. Implement tool-use, API integration, memory, and retrieval-augmented generation (RAG) within agents. Develop workflows for multi-agent collaboration, delegation, and orchestration. Fine-tune and optimize LLMs and agent frameworks (e.g., LangChain, LlamaIndex, Haystack, Semantic Kernel, custom implementations). Research and apply methods for reflection, error recovery, and self-correction in agents. Integrate agents into production environments, ensuring scalability, security, and observability. Work closely with product teams to design use cases (copilots, task automation, reasoning systems, etc.). Stay updated with emerging approaches in autonomous AI, multi-modal agents, and reinforcement learning for LLMs. Required Qualifications Bachelor’s/Master’s in Computer Science, AI, Data Science, or related field. Strong programming experience in Python (TypeScript/Go a plus). Hands-on experience with LLM orchestration frameworks (LangChain, LlamaIndex, AutoGen, Semantic Kernel). Knowledge of retrieval-augmented generation (RAG), vector databases (Pinecone, Weaviate, Milvus, FAISS). Experience with prompt engineering, fine-tuning, and LLM APIs (OpenAI, Anthropic, Mistral, LLaMA, etc.). Familiarity with MLOps for LLMs — model deployment, monitoring, evaluation, and feedback loops. Strong problem-solving and system design skills. Preferred / Nice-to-Have Experience with autonomous agent frameworks (AutoGPT, BabyAGI, CrewAI, or custom). Research or practical work on reflection, planning, and reasoning in LLMs. Experience with multi-agent simulations, emergent behavior, or reinforcement learning. Knowledge of safe AI practices — alignment, hallucination control, and red-teaming agents. Open-source contributions in the agentic AI ecosystem. What We Offer Opportunity to lead next-generation agent development projects. Competitive pay, benefits, and growth opportunities. Work with a highly innovative team pushing the boundaries of generative AI. A culture that encourages experimentation, learning, and applied research.

Job Title: AI Engineer Agent Development Location: [On-site / Remote / Hybrid] Employment Type: [Full-time / Contract] About the Role We are looking for an AI Engineer specialized in agent development to design, build, and scale intelligent autonomous systems. This role will focus on LLM-powered agents, multi-step reasoning, tool integrations, and orchestration frameworks that allow AI systems to interact with APIs, data sources, and humans. You will be at the forefront of shaping how generative AI is applied in production — developing robust, safe, and scalable agentic architectures. Key Responsibilities Design, develop, and deploy AI agents capable of autonomous reasoning, planning, and multi-step task execution. Implement tool-use, API integration, memory, and retrieval-augmented generation (RAG) within agents. Develop workflows for multi-agent collaboration, delegation, and orchestration. Fine-tune and optimize LLMs and agent frameworks (e.g., LangChain, LlamaIndex, Haystack, Semantic Kernel, custom implementations). Research and apply methods for reflection, error recovery, and self-correction in agents. Integrate agents into production environments, ensuring scalability, security, and observability. Work closely with product teams to design use cases (copilots, task automation, reasoning systems, etc.). Stay updated with emerging approaches in autonomous AI, multi-modal agents, and reinforcement learning for LLMs. Required Qualifications Bachelor’s/Master’s in Computer Science, AI, Data Science, or related field. Strong programming experience in Python (TypeScript/Go a plus). Hands-on experience with LLM orchestration frameworks (LangChain, LlamaIndex, AutoGen, Semantic Kernel). Knowledge of retrieval-augmented generation (RAG), vector databases (Pinecone, Weaviate, Milvus, FAISS). Experience with prompt engineering, fine-tuning, and LLM APIs (OpenAI, Anthropic, Mistral, LLaMA, etc.). Familiarity with MLOps for LLMs — model deployment, monitoring, evaluation, and feedback loops. Strong problem-solving and system design skills. Preferred / Nice-to-Have Experience with autonomous agent frameworks (AutoGPT, BabyAGI, CrewAI, or custom). Research or practical work on reflection, planning, and reasoning in LLMs. Experience with multi-agent simulations, emergent behavior, or reinforcement learning. Knowledge of safe AI practices — alignment, hallucination control, and red-teaming agents. Open-source contributions in the agentic AI ecosystem. What We Offer Opportunity to lead next-generation agent development projects. Competitive pay, benefits, and growth opportunities. Work with a highly innovative team pushing the boundaries of generative AI. A culture that encourages experimentation, learning, and applied research.