The Role
Leads the architecture and delivery of production-grade agentic and generative AI systems, including LLM agents, RAG pipelines, multi-agent workflows, and scalable cloud applications. Guides technical architecture, production engineering, LLMOps, evaluation, observability, security, and responsible AI practices. Mentors AI/ML and backend engineers, conducts design and code reviews, and collaborates with product, data, platform, security, compliance, and executive stakeholders.
Summary Generated by Built In
𝗧𝗵𝗶𝘀 𝗿𝗼𝗹𝗲 𝗶𝘀 𝗳𝗼𝗿 𝗼𝗻𝗲 𝗼𝗳 𝘁𝗵𝗲 𝗪𝗲𝗲𝗸𝗱𝗮𝘆'𝘀 𝗰𝗹𝗶𝗲𝗻𝘁𝘀
𝗦𝗮𝗹𝗮𝗿𝘆 𝗿𝗮𝗻𝗴𝗲: 𝗥𝘀 𝟯𝟬𝟬𝟬𝟬𝟬𝟬 - 𝗥𝘀 𝟱𝟬𝟬𝟬𝟬𝟬𝟬 (𝗶𝗲 𝗜𝗡𝗥 𝟯𝟬-𝟱𝟬 𝗟𝗣𝗔)
Experience: 10+ yrs
Location: Remote (India)
Job Type: Full-time
We are looking for a highly experienced Senior Technical Lead – Agentic AI / Generative AI to own the architecture, technical direction, and delivery of production-grade AI solutions. This is a hands-on leadership role for someone who can move seamlessly from early-stage experimentation and prototyping to scalable enterprise production systems.
You will design and build LLM-powered agentic systems, RAG architectures, multi-agent workflows, and AI applications, while mentoring a team of AI/ML and backend engineers. You will also work closely with Product, Data, Platform, Security, and other stakeholders to turn emerging GenAI capabilities into reliable, scalable, and business-ready solutions.
RequirementsKey ResponsibilitiesAI Architecture & Development
- 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 frameworks such as 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 appropriate 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.
Skills Required
- 10+ years of overall software engineering experience
- 4+ years of direct experience working with AI/ML systems
- 2+ years of hands-on experience building and deploying LLM-based or agentic AI applications in production
- Expertise in LLM application development, RAG, embeddings, vector databases, prompt engineering, and AI agents
- Experience with multi-agent systems, tool/function calling, memory management, planning, and reasoning workflows
- Strong Python and software engineering fundamentals
- 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 or LLMOps tools such as MLflow, LangSmith, or Weights & Biases
- Working knowledge of LLM fine-tuning and evaluation techniques, including LoRA/PEFT, RLHF concepts, and offline/online evaluation frameworks
- Proven technical leadership, architecture ownership, engineer mentoring, and cross-functional collaboration
- Strong communication skills for explaining technical concepts to business and executive stakeholders
- Experience deploying and fine-tuning open-source models such as Llama or Mistral
- Contributions to AI or GenAI open-source projects, technical publications, or conference presentations
- Experience building AI solutions in regulated industries such as finance, healthcare, or telecom
- Knowledge of AI guardrails, red-teaming, responsible AI, and model safety or evaluation frameworks
- Previous formal people-management experience
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The Company
What We Do
Weekday is an AI-powered recruitment platform that helps startups hire top-tier engineering and product talent. By leveraging a massive database of white-collar professionals and advanced outreach tools, the company streamlines the hiring process through automated sourcing, AI-driven resume screening, and white-glove contingency services. Their mission is to modernize recruitment by enabling companies to discover and engage passive candidates efficiently, ensuring high-quality hires for critical roles.
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