Senior Agentic AI Engineer

Opportunity

AI engineering and product delivery

• Design, build, and deploy production-grade AI agent applications using self-hosted LLMs and modern agentic frameworks.
• Build agent workflows that integrate with enterprise systems, internal applications, and business processes.
• Develop internal tools, user portals, and backend services for AI-powered applications and automation.
• Contribute to the development, integration, and optimization of commercial AI products, balancing performance, scalability, cost, and time-to-market.

Security, infrastructure, and lifecycle

• Develop secure, private AI architectures with data isolation, access controls, encryption, and auditability.
• Own deployment, monitoring, versioning, performance optimization, and continuous improvement across the AI lifecycle.
• Implement scalable infrastructure and MLOps using Docker, Kubernetes, CI/CD, and cloud or on-premises environments.
• Work with product, engineering, and business stakeholders to translate requirements into technical solutions and measure impact.
• Provide technical leadership, share best practices, and mentor engineers while remaining hands-on.

Experience and AI expertise

• 5+ years of AI/ML Engineering experience; product-focused or fast-paced technology experience is ideal.
• Hands-on experience building and deploying production-grade Generative AI and Agentic AI systems.
• Experience deploying self-hosted LLMs in private or on-premises environments without external AI API dependence.
• Experience with LLaMA, Mistral, GPT, or Claude and frameworks such as LangChain, Semantic Kernel, CrewAI, or MCP-based architectures.
• Experience with RAG, prompt engineering, LLM evaluation, LoRA/QLoRA fine-tuning, and model optimization.

Engineering and security skills

• Strong Python skills and experience building scalable AI workflows, backend services, and production applications.
• Experience with Docker, Kubernetes, CI/CD, MLOps, and AI lifecycle management.
• Knowledge of secure system design, including authentication, authorization, data isolation, encryption, and audit logging.
• Experience with SQL/NoSQL databases, data modeling, ETL/ELT pipelines, and large-scale datasets.
• Experience deploying and optimizing AI across on-premises and cloud environments; AWS exposure is advantageous.

Additional qualifications

• Strong product ownership, problem-solving, and communication skills, including translating business requirements into production-ready technical solutions.
• A Master’s degree in Computer Science, Data Science, or a related technical discipline is preferred.