Senior Technical Lead – Agentic AI, Generative AI

🕒 August 19

🇮🇳 India – Remote

💵 ₹3M - ₹5M / year

⏰ Full Time

🟠 Senior

🧑‍💻 Full-stack Engineer

👻 Ghost score 24%

What it means: how likely this posting is sitting open without active hiring.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

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Weekday (YC W21)

WebsiteLinkedInAll Job Openings

11 - 50 employees

Founded 2021

💼 Consulting

👥 HR Tech

☁️ SaaS

Consulting • HR Tech • SaaS

Weekday is a modern recruitment platform that combines AI technologies with a vast database of potential candidates, aiming to streamline the hiring process for companies in India. They offer various services, including a proactive outreach approach that helps employers connect with top talent, as well as tools for candidates to easily apply for jobs. Weekday's emphasis on candidate engagement through multiple channels, including email, WhatsApp, and phone calls, sets it apart in the competitive landscape of recruitment agencies.

📋 Description

• 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 for 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 to senior leadership and business stakeholders • Represent the AI engineering function in strategic GenAI technology and roadmap discussions

🎯 Requirements

• 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