Space Inventive
Website:
spaceinventive.com
Job details:
Space Inventive is an innovative and dynamic company that specializes in leading businesses through transformative journeys in the digital era. They are pioneers in driving innovation and helping organizations transition into digitally mature entities. With a wide range of cutting-edge services, including web enterprise application development, AI & ML development, cloud engineering, data engineering, and business intelligence, Space craft's tailor-made solutions to meet each client's unique challenges. Their integrated approach combines strategic vision with digital expertise, empowering businesses to create new models, modernize legacy systems, and launch market-ready digital products and platforms.
Role Summary :
We are seeking a highly skilled Technical Architect to design, architect, and deliver next-generation AI solutions leveraging LLMs, Agentic AI, Multi-Agent Systems, and AWS Cloud. The role involves building scalable and intelligent AI applications, designing advanced agent orchestration frameworks, and implementing A2A and MCP-based architectures. You will lead the end-to-end architecture and development of enterprise-grade GenAI solutions, ensuring scalability, performance, security, and reliability. Working closely with cross-functional teams, you will translate business requirements into innovative AI products while driving technical excellence and architectural best practices. The ideal candidate combines deep expertise in AI/ML, cloud-native architectures, prompt engineering, and distributed AI systems with strong hands-on development experience.
Key Responsibilities :
- Design, architect, and deploy scalable agentic AI systems from concept to production.
- Lead system design for AI/ML and GenAI solutions, ensuring scalability, reliability, and performance.
- Build and optimize advanced agentic workflows including multi-agent orchestration, memory, tool usage, and reasoning pipelines.
- Design and implement Agent-to-Agent (A2A) communication protocols for seamless coordination between distributed AI agents.
- Integrate and manage Model Context Protocols (MCPs) to enable structured context sharing, tool interoperability, and dynamic knowledge access for LLM-based systems.
- Utilize and fine-tune Large Language Models (LLMs) to build intelligent, responsive, and scalable applications.
- Apply expert-level prompt engineering techniques to optimize model performance, accuracy, and efficiency.
- Develop and deploy AI solutions on AWS cloud infrastructure (e.g., ECS, Lambda, S3, Bedrock, API Gateway).
- Write clean, robust, and maintainable code, primarily in Python, following best practices for software development.
- Collaborate with cross-functional teams, including product managers, data scientists, and engineers, to define requirements and deliver high-quality AI features.
- Stay current with the latest advancements in AI, machine learning, NLP, and GenAI ecosystems.
- Mentor junior engineers and contribute to a culture of technical excellence and continuous learning.
Required Qualification and Skills :
- BTech/MTech in Computer Science, Artificial Intelligence, or a related field.
- 5+ years of professional experience in AI/ML engineering with a strong portfolio of production-grade systems.
- Expert proficiency in Python and AI/ML frameworks (TensorFlow, PyTorch, Hugging Face).
- Strong experience in system design for scalable AI/ML and GenAI architectures.
- Hands-on experience with advanced agentic AI concepts such as planning, reasoning, tool use, memory management, and multi-agent systems.
- Experience implementing A2A protocols for distributed agent communication and coordination.
- Familiarity with Model Context Protocols (MCPs) or similar frameworks for context orchestration and tool integration.
- Deep understanding and practical experience with Large Language Models (LLMs).
- Proven expertise in prompt engineering, evaluation, and optimization techniques.
- Experience working with AWS cloud services for deploying and scaling AI solutions.
- Strong problem-solving skills and analytical mindset.
- Excellent communication and collaboration skills in a hybrid work environment.
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