Senior AI Engineer

Accountabilities: Lead complex technical engagements with enterprise customers, taking responsibility for delivery quality, stakeholder relationships, and project outcomes. Manage multiple concurrent workstreams, coordinating resources, milestones, workplans, and cross-functional teams. Act as the primary technical escalation point, identifying delivery risks and driving resolutions across engineering, product, and leadership teams. Build agentic AI systems and multi-agent frameworks to automate complex enterprise workflows. Design and deploy LLM-powered applications using retrieval-augmented generation (RAG), fine-tuning, prompt engineering, function calling, and tool integration. Implement AI guardrails, evaluation frameworks, and responsible AI controls to ensure reliability, safety, and production readiness. Own the full AI development lifecycle, from problem framing and data exploration to model development, API integration, and production deployment. Build scalable backend services, APIs, ML pipelines, and LLMOps infrastructure to support enterprise AI applications and continuous improvement. Integrate AI solutions into cloud, on-premises, and hybrid environments, ensuring performance, stability, and maintainability. Lead technical architecture decisions, review engineering deliverables, and maintain high standards across project teams. Mentor junior engineers and collaborate with product and engineering teams to incorporate customer feedback and resolve platform-level challenges. Lead technical demonstrations, proof-of-concept initiatives, customer workshops, executive briefings, and technical design discussions. Requirements: At least 5 years of hands-on AI/ML engineering experience, including end-to-end model development and deployment in production environments. Demonstrated experience leading complex enterprise technical engagements involving multiple stakeholders and cross-functional teams. Proven experience building and deploying LLM-powered applications, including RAG pipelines, agentic workflows, or fine-tuned models. Strong Python programming skills and practical experience with machine learning frameworks such as PyTorch, TensorFlow, and scikit-learn. Familiarity with LLM development tools and frameworks such as LangChain, LlamaIndex, or equivalent technologies. Experience deploying AI services in enterprise or cloud environments, including AWS, Azure, GCP, or on-premises Kubernetes. Strong understanding of generative AI concepts, including prompt engineering, RAG, fine-tuning, RLHF, model evaluation, guardrails, and LLMOps. Solid foundation in classical machine learning and the ability to select appropriate approaches based on business and technical requirements. Backend engineering experience, including REST APIs, Docker, Kubernetes, and CI/CD pipelines. Ability to coordinate multiple delivery streams, manage competing priorities, and guide teams through evolving requirements. Excellent communication and stakeholder management skills, with the confidence to present to executive audiences and lead technical discussions. Adaptability, sound technical judgment, and the ability to establish direction in ambiguous or fast-changing environments. Additional experience that would be an advantage: Kaggle participation or competitive machine learning experience. Familiarity with enterprise AI platforms, AutoML, tabular foundation models, or document AI solutions. Experience delivering AI solutions in regulated industries such as financial services or healthcare. Previous experience in customer-facing engineering, field engineering, or technical pre-sales roles. Benefits: Competitive total rewards package. Remote-friendly culture. Flexible working environment. Opportunity to work with a world-class team of AI professionals. Career growth and professional development opportunities.