Software Engineer (Agentic AI)
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Key skills for this role
About the Role
A dedicated startup is being formed to industrialize a secure, AI-enabled decision-support software platform. The Agentic AI Engineer will design, develop, and deploy intelligent autonomous systems with minimal human intervention.
Key Skills for This Role
Responsibilities
- Design and implement agentic AI systems capable of autonomous decision making, tool use, and multi step task execution
- Architect and build multi agent frameworks, including planning, coordination, memory, and feedback loops
- Integrate LLMs and other AI models with external tools, APIs, databases, and enterprise systems
- Develop robust agent orchestration layers, including task routing, context management, and safety controls
- Implement scalable backend services to support agent execution, state management, and event driven workflows
- Optimize agent performance through prompt engineering, model fine tuning, retrieval augmented generation (RAG), and caching strategies
- Collaborate with product, data, and engineering teams to design end to end agentic solutions for real business use cases
- Ensure agent systems adhere to security, privacy, and compliance requirements
- Build monitoring, logging, and evaluation pipelines to track agent behavior, reliability, and performance
- Conduct root cause analysis and debugging for complex agent behaviors and emergent system issues
- Maintain documentation for agent architectures, design patterns, and operational procedures
Requirements
- Typically 5+ years of relevant experience in a fast paced product environment
- Bachelor's or Master's degree in Computer Science, Engineering, AI/ML, or equivalent practical experience
- 3 5 years of experience in AI engineering, machine learning, backend engineering, or related fields
- Strong hands on experience with LLMs, AI frameworks, and agentic toolchains (LangChain, LlamaIndex, OpenAI tools, custom agent frameworks, etc.)
- Proficiency in Python (required) and familiarity with additional languages such as TypeScript, or C++ is a plus
- Solid understanding of software architecture, distributed systems, and event driven design
- Experience building systems that integrate with APIs, databases, vector stores, and messaging brokers
- Familiarity with RAG pipelines, embeddings, vector databases, and model fine tuning
- Experience with cloud platforms, like AWS, and container orchestration (Docker, Kubernetes)
- Knowledge of CI/CD pipelines, GitHub workflows, and modern DevOps practices
Full Job Posting
Job Role
- A dedicated startup is being formed to industrialize and scale a secure, AI enabled, multi source decision support software offering.
- The platform is a multi sensor fusion and agentic AI solution connecting to diverse data sources (for example geospatial layers, imagery, video, and other operational signals).
- This role will support the delivery of a scalable product and contribute to establishing the processes, standards, and collaboration practices required for sustainable growth.
- The Agentic AI Engineer is responsible for designing, developing, and deploying intelligent autonomous systems that can reason, plan, and act with minimal human intervention.
Job Responsibilities
- Design and implement agentic AI systems capable of autonomous decision making, tool use, and multi step task execution.
- Architect and build multi agent frameworks, including planning, coordination, memory, and feedback loops.
- Integrate LLMs and other AI models with external tools, APIs, databases, and enterprise systems.
- Develop robust agent orchestration layers, including task routing, context management, and safety controls.
- Implement scalable backend services to support agent execution, state management, and event driven workflows.
- Optimize agent performance through prompt engineering, model fine tuning, retrieval augmented generation (RAG), and caching strategies.
- Collaborate with product, data, and engineering teams to design end to end agentic solutions for real business use cases.
- Ensure agent systems adhere to security, privacy, and compliance requirements.
- Build monitoring, logging, and evaluation pipelines to track agent behavior, reliability, and performance.
- Conduct root cause analysis and debugging for complex agent behaviors and emergent system issues.
- Maintain documentation for agent architectures, design patterns, and operational procedures.
Qualifications & Experience
- Typically 5+ years of relevant experience in a fast paced product environment.
- Bachelor's or Master's degree in Computer Science, Engineering, AI/ML, or equivalent practical experience.
- 3 5 years of experience in AI engineering, machine learning, backend engineering, or related fields.
- Strong hands on experience with LLMs, AI frameworks, and agentic toolchains (LangChain, LlamaIndex, OpenAI tools, custom agent frameworks, etc.).
- Proficiency in Python (required) and familiarity with additional languages such as TypeScript, or C++ is a plus.
- Solid understanding of software architecture, distributed systems, and event driven design.
- Experience building systems that integrate with APIs, databases, vector stores, and messaging brokers.
- Familiarity with RAG pipelines, embeddings, vector databases, and model fine tuning.
- Experience with cloud platforms, like AWS, and container orchestration (Docker, Kubernetes).
- Knowledge of CI/CD pipelines, GitHub workflows, and modern DevOps practices.
- Understanding of AI safety, evaluation, and guardrail frameworks is a plus.
Competencies
- Strong analytical and problem solving skills, especially in complex or ambiguous AI driven systems.
- Ability to design scalable, modular, and resilient agentic architectures.
- Excellent communication and cross functional collaboration abilities.
- High attention to detail and commitment to reliability and safety in AI systems.
- Proactive mindset with a passion for innovation, automation, and continuous improvement.
- Ability to work independently, manage multiple priorities, and drive projects from concept to deployment.
- Strong sense of ownership and accountability for system behavior and outcomes.
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