Senior Technical Lead - Agentic AI

Accountabilities

• • AI architecture and development: Architect, design, and develop Agentic AI and Generative AI solutions from concept and prototyping through production, including multi-step reasoning agents, tool/function-calling workflows, and multi-agent systems.
  • RAG and LLM applications: Build scalable RAG pipelines covering chunking, embeddings, vector search, hybrid retrieval, prompt engineering, model routing, fine-tuning, and optimization.
  • Model strategy: Evaluate and select foundation models based on accuracy, performance, latency, cost, and business requirements.
  • Production engineering: Own technical architecture decisions for reliable, scalable, secure, and cost-efficient LLM applications, including APIs, microservices, and cloud-native architectures.
  • AI/LLMOps: Establish practices for model lifecycle management, deployment, monitoring, evaluation, observability, guardrails, hallucination mitigation, and continuous improvement.
  • Technical leadership: Lead and mentor AI/ML and backend engineers, conduct architecture and design reviews, participate in code reviews, and establish strong engineering standards for production AI.
  • Cross-functional collaboration: Partner with Product, Data Science, Platform, Security, and Compliance teams to ensure AI solutions align with business objectives, privacy requirements, security standards, and responsible-AI principles.
  • Strategic communication: Communicate complex AI concepts and technical decisions effectively to senior leadership and business stakeholders while contributing to GenAI technology strategy and roadmap discussions.

  Requirements

  • Experience: 10+ years of overall software engineering experience, including at least 4 years working directly with AI/ML systems and 2+ years building and deploying LLM-based or Agentic AI applications in production.
  • Generative AI expertise: Strong hands-on knowledge of LLM application development, RAG, embeddings, vector databases, prompt engineering, AI agents, memory management, planning, reasoning, and tool/function calling.
  • Agentic AI frameworks: Practical experience with frameworks such as LangGraph, AutoGen, CrewAI, or equivalent custom orchestration approaches.
  • Software engineering: Strong Python and software engineering fundamentals, with experience developing scalable, distributed, production-grade systems.
  • Cloud and architecture: Experience with APIs, microservices, cloud-native architectures, and at least one major cloud platform such as AWS, Azure, or GCP.
  • MLOps/LLMOps: Hands-on experience with platforms or tools such as MLflow, LangSmith, Weights & Biases, or equivalent solutions.
  • Model optimization: Working knowledge of fine-tuning and evaluation techniques, including LoRA/PEFT, RLHF concepts, and offline and online evaluation frameworks.
  • Leadership: Demonstrated ability to own architecture decisions, provide technical direction, mentor engineers, conduct technical reviews, and collaborate effectively across functions.
  • Communication: Excellent communication skills with the ability to translate complex technical concepts into clear business and executive-level discussions.
  • Preferred expertise: Experience deploying or fine-tuning open-source models such as Llama or Mistral, contributing to AI/GenAI open-source projects or publications, or working on AI solutions in regulated industries such as finance, healthcare, or telecom.
  • Responsible AI: Knowledge of AI guardrails, red-teaming, responsible AI, model safety, and evaluation frameworks is advantageous.
  • Additional leadership experience: Previous formal people-management experience is a plus.

  Benefits

  • Competitive compensation: ₹30,00,000–₹50,00,000 per year.
  • Remote flexibility: Fully remote position based in India.
  • Full-time opportunity: Permanent, full-time role with significant technical ownership and leadership scope.
  • AI innovation: Opportunity to work hands-on with Agentic AI, Generative AI, LLMs, RAG, and emerging AI technologies.
  • Leadership growth: Ability to mentor engineering talent and influence technical standards, architecture, and AI strategy.
  • Cross-functional exposure: Collaborate with Product, Data, Platform, Security, Compliance, and senior business stakeholders.
  • Enterprise impact: Build scalable, production-ready AI solutions designed to deliver measurable business value.