Senior AI/ML Engineer

Key Responsibilities

• Design, develop, and maintain production-grade AI and ML solutions across cloud and hybrid platforms
• Build end-to-end AI application pipelines including data ingestion, prompt engineering, model evaluation, deployment, and monitoring
• Develop and support LLM-powered applications and agentic workflows
• Collaborate with engineering, data, and business stakeholders to translate requirements into AI and ML solutions
• Write scalable, maintainable, and high-quality Python code for AI and ML applications
• Optimize performance, scalability, reliability, and cost efficiency of AI and ML services
• Integrate AI and ML applications with enterprise APIs, data lakes, data warehouses, retrieval systems (RAG), and streaming platforms
• Support AI solution lifecycle management including versioning, prompt iteration, evaluation, and production support
• Apply security, governance, compliance, and data privacy best practices across AI and ML systems
• Participate in technical architecture and solution design discussions
• Stay current on advancements in AI, ML, LLMs, cloud engineering, and orchestration technologies

Required Qualifications

• 5+ years of experience building and deploying AI and ML solutions
• Strong hands-on experience developing AI and ML applications within cloud environments
• Advanced Python programming skills focused on AI/ML engineering and LLM application development
• Experience building and supporting LLM-powered applications, agentic workflows, and AI orchestration pipelines
• Hands-on experience with AI orchestration and observability frameworks such as LangChain, LangSmith, Pydantic AI, or similar technologies
• Strong understanding of cloud security, AI governance, data privacy, and enterprise AI compliance practices
• Experience developing scalable, production-grade cloud-native applications

Preferred Qualifications

• Experience supporting enterprise AI initiatives within professional services or regulated environments
• Familiarity with vector databases, embedding models, semantic search, and AI evaluation frameworks
• Experience with containerization and deployment technologies such as Docker and Kubernetes
• Exposure to MLOps and AI observability best practices
• Experience integrating AI systems into enterprise business workflows and knowledge management platforms