Senior AI/ML Engineer

• Architect end-to-end agentic AI systems including agent design, orchestration, and integration with enterprise systems
  
• Design advanced agent patterns including hierarchical agents, multi-agent collaboration, and dynamic tool management
  
• Lead the development of sophisticated reasoning architectures with planning, reflection, and self-correction mechanisms
  
• Establish and implement evaluation frameworks, bench-marking methodologies, and cost optimization strategies for agentic systems
  
• Build scalable prompt engineering pipelines and fine-tune LLMs for specific use cases and domains
  
• Design and implement RAG systems with advanced retrieval strategies, knowledge management, and semantic search
  
• Develop production-grade monitoring, observability, and error handling for AI systems at scale
  
• Mentor junior engineers on agentic AI patterns, best practices, and system design principles
  
• Lead technical design reviews and architectural decisions for AI/ML initiatives
  
• Collaborate with product, infrastructure, and security teams to ensure responsible AI deployment
  
• Drive innovation in agentic AI approaches and evaluate emerging frameworks and models
  
• Optimize system performance, latency, and costs across production agentic workflows
  
• Establish CI/CD and testing practices specific to AI/ML systems
  

Requirements

• 5-7 years of professional software development or machine learning experience, with 2+ years focused on agentic AI
  
• Proven track record building production agentic AI systems with measurable business impact
  
• Expert-level proficiency in Python and software engineering best practices (design patterns, testing, documentation)
  
• Deep experience with multiple LLM providers and APIs (OpenAI, Anthropic Claude, Azure OpenAI, open-source models)
  
• Advanced knowledge of prompt engineering, prompt chaining, chain-of-thought reasoning, and few-shot learning
  
• Hands-on expertise with agentic frameworks (LangChain, CrewAI, Autogen, Semantic Kernel, or equivalent)
  
• Strong understanding of tool use, function calling, and dynamic agent capability management
  
• Experience architecting retrieval-augmented generation (RAG) systems with vector databases and semantic search
  
• Knowledge of model fine-tuning, domain adaptation, and custom model training
  
• Proficiency with asynchronous programming and handling high-concurrency systems
  
• Experience with API design and integration patterns for complex distributed systems
  
• Strong background in software testing, including evaluation frameworks for AI systems
  

• Experience deploying and managing agentic systems in production at scale
  
• Cloud platform expertise (Azure, AWS, GCP) for AI/ML services and infrastructure
  
• Background building observability, monitoring, and debugging tools for ML systems
  
• Experience with multi-agent systems, agent communication protocols, and collaborative agent patterns
  
• Knowledge of reinforcement learning from human feedback (RLHF) or similar alignment techniques
  
• Familiarity with knowledge graphs, semantic databases, and advanced retrieval strategies
  
• Experience optimizing token usage and managing costs for LLM-based applications
  
• Background in enterprise software or complex system integration
  
• Experience with MLOps, model versioning, and continuous integration for AI systems
  
• Leadership or mentoring experience with junior engineers
  

• Published research, papers, or significant open-source contributions in AI/ML
  
• Experience with agent-as-a-service architectures or API platforms
  
• Background in building customer-facing AI products
  
• Knowledge of adversarial testing and robustness evaluation for AI systems
  
• Experience with specialized domains (financial services, healthcare, legal, etc.)
  
• Understanding of AI safety, alignment, and ethical considerations in agent design
  
• Experience building domain-specific large language models or specialized agent systems