Senior Technical Lead – Generative AI

• Architect and develop Agentic AI and Generative AI systems from concept through production
• Build multi-step reasoning agents, tool/function-calling workflows, and multi-agent architectures using LangGraph, AutoGen, CrewAI, or custom orchestration
• Design and productionize scalable RAG pipelines, including chunking, embeddings, vector search, and hybrid retrieval
• Evaluate and select foundation models based on performance, accuracy, latency, cost, and business requirements
• Develop strategies for prompt engineering, model routing, fine-tuning, and optimization
• Own technical architecture decisions for scalable, reliable, and cost-efficient LLM applications
• Establish engineering standards covering testing, evaluation, observability, guardrails, hallucination mitigation, and production monitoring
• Design APIs, microservices, and cloud-native architectures supporting AI applications at scale
• Drive AI/LLMOps practices across model lifecycle management, deployment, monitoring, and continuous improvement
• Lead, mentor, and develop AI/ML and backend engineers
• Conduct technical design reviews, architecture discussions, and code reviews
• Establish engineering best practices and promote high standards for production AI development
• Provide technical direction while remaining actively involved in complex engineering problems
• Partner with Product, Data Science, Platform, Security, and Compliance teams to deliver AI solutions aligned with business objectives
• Ensure AI systems meet privacy, security, compliance, and responsible-AI requirements
• Communicate complex technical concepts clearly to senior leadership and business stakeholders
• Represent the AI engineering function in strategic discussions around GenAI technology and roadmap decisions

• 10+ years of overall software engineering experience, including 4+ years working directly with AI/ML systems
• At least 2+ years of hands-on experience building and deploying LLM-based or agentic AI applications in production
• Deep expertise in LLM application development, RAG, embeddings, vector databases, prompt engineering, and AI agents
• Practical experience with multi-agent systems, tool/function calling, memory management, planning, and reasoning workflows
• Strong Python and software engineering fundamentals with experience building scalable, distributed, production-grade systems
• Experience with APIs, microservices, cloud-native architecture, and at least one major cloud platform such as AWS, Azure, or GCP
• Hands-on experience with MLOps/LLMOps tools such as MLflow, LangSmith, Weights & Biases, or equivalent platforms
• Working knowledge of LLM fine-tuning and evaluation techniques, including LoRA/PEFT, RLHF concepts, and offline/online evaluation frameworks
• Proven ability to provide technical leadership, mentor engineers, own architecture decisions, and collaborate across teams
• Strong communication skills with the ability to translate complex technical concepts into clear business and executive-level discussions
• Experience deploying and fine-tuning open-source models such as Llama or Mistral, alongside proprietary models/APIs
• Contributions to AI/GenAI open-source projects, technical publications, or conference presentations
• Experience building AI solutions within regulated industries such as finance, healthcare, or telecom
• Knowledge of AI guardrails, red-teaming, responsible AI, and model safety/evaluation frameworks
• Previous formal people-management experience