Own the architecture and delivery of production-grade generative and agentic AI systems, including LLM agents, RAG pipelines, multi-agent workflows, APIs, and cloud-native services. Establish AI engineering, evaluation, observability, guardrail, and LLMOps practices while mentoring AI/ML and backend engineers. Collaborate with Product, Data, Platform, Security, and Compliance teams to deliver scalable, secure, and business-ready solutions, and provide technical direction on GenAI strategy and architecture.
๐ง๐ต๐ถ๐ ๐ฟ๐ผ๐น๐ฒ ๐ถ๐ ๐ณ๐ผ๐ฟ ๐ผ๐ป๐ฒ ๐ผ๐ณ ๐๐ต๐ฒ ๐ช๐ฒ๐ฒ๐ธ๐ฑ๐ฎ๐'๐ ๐ฐ๐น๐ถ๐ฒ๐ป๐๐
๐ฆ๐ฎ๐น๐ฎ๐ฟ๐ ๐ฟ๐ฎ๐ป๐ด๐ฒ: ๐ฅ๐ ๐ฏ๐ฌ๐ฌ๐ฌ๐ฌ๐ฌ๐ฌ - ๐ฅ๐ ๐ฑ๐ฌ๐ฌ๐ฌ๐ฌ๐ฌ๐ฌ (๐ถ๐ฒ ๐๐ก๐ฅ ๐ฏ๐ฌ-๐ฑ๐ฌ ๐๐ฃ๐)
Experience: 10+ yrs
Location: Remote (India)
Job Type: Full-time
We are looking for a highly experienced Senior Technical Lead โ 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.
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