Senior Technical Lead - Generative AI

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Senior Technical Lead - Generative AI based in India.

This is a hands-on technical leadership role focused on designing and delivering production-grade Generative AI and Agentic AI solutions. You will own the architecture and technical direction of LLM-powered applications, moving ideas from experimentation and prototypes into scalable enterprise systems. The role covers agentic workflows, multi-agent architectures, RAG pipelines, model evaluation, and AI/LLMOps. You will combine deep engineering expertise with technical leadership, mentoring AI/ML and backend engineers while remaining involved in complex technical challenges. Collaboration spans Product, Data, Platform, Security, Compliance, and senior business stakeholders. The role offers an opportunity to shape GenAI strategy while building reliable, secure, observable, and cost-efficient AI products.

Accountabilities

• Architect, design, and develop Agentic AI and Generative AI solutions from early concepts and prototypes through production deployment.
• Build multi-step reasoning agents, tool and function-calling workflows, memory systems, planning capabilities, and multi-agent architectures using frameworks such as LangGraph, AutoGen, CrewAI, or custom orchestration.
• Design and productionize scalable Retrieval-Augmented Generation (RAG) pipelines covering chunking, embeddings, vector search, hybrid retrieval, and related retrieval strategies.
• Evaluate and select foundation models based on accuracy, performance, latency, cost, and business requirements.
• Develop and implement strategies for prompt engineering, model routing, fine-tuning, optimization, and continuous model improvement.
• Own technical architecture decisions for reliable, scalable, secure, and cost-efficient LLM applications.
• Establish engineering standards for AI testing, evaluation, observability, guardrails, hallucination mitigation, monitoring, and production reliability.
• Design APIs, microservices, and cloud-native architectures that support AI applications at enterprise scale.
• Drive AI/LLMOps practices covering model lifecycle management, deployment, monitoring, evaluation, and continuous improvement.
• Lead, mentor, and develop AI/ML and backend engineering teams while maintaining strong technical standards.
• Conduct architecture and technical design reviews, code reviews, and engineering discussions.
• Remain hands-on with complex engineering challenges and provide technical direction across AI initiatives.
• Partner with Product, Data Science, Platform, Security, and Compliance teams to align AI solutions with business objectives and organizational requirements.
• Ensure AI systems address privacy, security, compliance, responsible-AI, and model-safety considerations.
• Communicate complex AI and engineering concepts clearly to senior leadership and business stakeholders.
• Represent the AI engineering function in strategic technology discussions, roadmap planning, and GenAI initiatives.

Requirements

• 10+ years of overall software engineering experience, including at least 4 years working directly with AI/ML systems.
• 2+ years of hands-on experience building and deploying LLM-based or Agentic AI applications in production environments.
• Deep expertise in LLM application development, RAG, embeddings, vector databases, prompt engineering, and AI-agent architectures.
• 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 designing APIs, microservices, cloud-native architectures, and applications on at least one major cloud platform such as AWS, Azure, or GCP.
• Hands-on experience with MLOps or LLMOps platforms such as MLflow, LangSmith, Weights & Biases, or equivalent technologies.
• Working knowledge of LLM fine-tuning and evaluation techniques, including LoRA/PEFT, RLHF concepts, and offline/online evaluation frameworks.
• Strong understanding of production AI engineering practices, including testing, observability, monitoring, guardrails, hallucination mitigation, and model evaluation.
• Proven technical leadership experience, including architecture ownership, mentoring engineers, technical reviews, and cross-functional collaboration.
• Excellent communication and stakeholder-management skills, with the ability to translate complex technical concepts into clear business and executive-level discussions.
• Experience deploying or fine-tuning open-source models such as Llama or Mistral alongside proprietary models or APIs is advantageous.
• Contributions to AI/GenAI open-source projects, technical publications, or conference presentations are a plus.
• Experience developing AI solutions in regulated industries such as finance, healthcare, or telecommunications is desirable.
• Knowledge of AI guardrails, red-teaming, responsible AI, model safety, and evaluation frameworks is beneficial.
• Previous formal people-management experience is a plus.

Benefits

• Competitive annual compensation of approximately INR 30-50 LPA, depending on experience and skills.
• Fully remote, full-time opportunity available across India.
• Opportunity to lead the architecture and delivery of cutting-edge Generative AI and Agentic AI solutions.
• Hands-on exposure to LLMs, multi-agent systems, RAG, LLMOps, cloud-native architectures, and emerging AI technologies.
• Significant technical ownership and influence over AI engineering standards, architecture, and roadmap decisions.
• Opportunity to mentor and develop AI/ML and backend engineering talent.
• Cross-functional collaboration with Product, Data, Platform, Security, Compliance, and senior leadership teams.
• Opportunity to solve complex enterprise problems and transition emerging AI capabilities into production-ready solutions.
• Potential exposure to responsible AI, model safety, evaluation, and AI solutions in highly regulated environments.