Technical Lead - AI & Engineering

Tech Lead - Engineering & AIAbout the Role : We are looking for a highly capable and hands-on Tech Lead - Engineering & AI to lead the architecture, development, and deployment of scalable software and AI-driven products.The role combines technical leadership, full-stack engineering, AI/LLM engineering, system architecture, DevOps/MLOps, and team mentorship. The ideal candidate will be comfortable working across both conventional software systems and modern AI architectures, including RAG pipelines, agentic workflows, LLM-powered applications, and AI production infrastructure.You will play a key role in shaping technical direction, driving engineering excellence, and taking products and AI capabilities from concept and architecture through production deployment and continuous optimization.Key Responsibilities : Technical Leadership & Architecture : - Lead the design and implementation of scalable, reliable, and maintainable software architectures.- Architect solutions across the MERN/MEAN ecosystem and modern AI/ML systems.- Design and review architectures involving RAG pipelines, agentic workflows, LLM-powered applications, and AI-driven products.- Identify architectural bottlenecks and drive improvements in system reliability, scalability, security, and performance.- Establish engineering best practices around coding standards, architecture, testing, and technical documentation.Team Leadership & Mentorship : - Lead and mentor a team of software engineers and AI/ML engineers.- Conduct code reviews, architecture reviews, and prompt/evaluation reviews.- Provide technical guidance and support engineers in solving complex development and production challenges.- Break down complex technical requirements into actionable tasks and ensure effective execution.- Foster a culture of ownership, technical excellence, collaboration, and continuous learning.AI & LLM Engineering : - Design and develop production-grade applications powered by LLMs and generative AI.- Build and optimize RAG pipelines, agentic systems, LLM workflows, and AI-driven analytics solutions.- Work with LLM platforms and APIs such as OpenAI, Anthropic, Gemini, and open-source models.- Design multi-model and multi-tier orchestration strategies based on performance, cost, latency, and accuracy requirements.- Implement evaluation frameworks to measure and continuously improve AI system quality.- Optimize prompts, retrieval strategies, model selection, token usage, and inference performance.DevOps, MLOps & Infrastructure : - Own and improve CI/CD pipelines, infrastructure, and production deployment processes.- Design and manage deployment pipelines for both conventional applications and AI/ML workloads.- Work with containerized environments and cloud infrastructure across AWS, Azure, or GCP.- Manage infrastructure supporting model inference, vector databases, APIs, and AI workloads.- Implement appropriate monitoring, logging, observability, and reliability practices.- Evaluate and implement serverless and scalable infrastructure patterns where appropriate.Release & Delivery Management : - Drive the complete software and AI feature release lifecycle from development through production.- Coordinate with engineering, product, and other stakeholders to ensure timely and high-quality releases.- Establish effective release processes, deployment standards, and rollback strategies.- Identify and proactively address technical risks that may impact delivery timelines or production stability.Hands-on Engineering : - Remain hands-on with development while providing technical leadership.- Contribute directly to solving complex architectural, backend, AI/ML, and infrastructure problems.- Debug production issues across application, infrastructure, and AI/LLM layers.- Improve system performance, retrieval quality, model behavior, and application reliability.Cost & Performance Optimization : - Own the cost-performance trade-offs associated with production AI systems.- Optimize LLM selection, token consumption, prompt efficiency, inference costs, and latency.- Monitor AI infrastructure and API costs and identify opportunities for optimization.- Balance system accuracy, scalability, latency, reliability, and operating costs.Technical Requirements : Core Engineering : - 4+ years of professional software development experience.- At least 1+ year of experience in a technical leadership or senior engineering capacity.- Strong hands-on expertise in Python and Node.js.- Strong experience with at least one modern frontend framework such as React or Angular.- Strong working knowledge of Express.js, PostgreSQL, and MongoDB.- Strong understanding of REST APIs, database design, distributed systems, and system architecture.AI / LLM Engineering : - Hands-on experience building and deploying LLM-powered applications.- Strong experience with LLM APIs/platforms such as OpenAI, Anthropic, Gemini, or open-source models.- Experience with RAG architectures, Vector databases, Agentic workflows, Prompt engineering, LLM evaluation, Model orchestration, and AI/ML production systems.- Experience with vector databases such as pgvector, Pinecone, Weaviate, or equivalent technologies.- Understanding of LLM latency, accuracy, scalability, and cost trade-offs.Infrastructure & DevOps : - Strong understanding of CI/CD pipelines using tools such as Jenkins, GitHub Actions, or GitLab CI.- Experience with Docker/containerization and cloud deployment.- Working knowledge of AWS, Azure, or GCP.- Understanding of MLOps concepts, model deployment, inference infrastructure, monitoring, and observability.- Familiarity with serverless architectures and cloud-native patterns is a plus.Project Experience : Candidates should have successfully delivered 3+ significant projects end-to-end, covering multiple stages such as Architecture, Development, Testing, Deployment, Production, and Optimization.At least some of these projects should involve meaningful AI/ML or LLM engineering, such as RAG-based applications, LLM-powered products, Agentic AI systems, AI-driven analytics, LLM fine-tuning, AI automation platforms, or production-grade generative AI systems.Candidates should be prepared to clearly explain their individual contribution, architectural decisions, technical challenges, and measurable outcomes for these projects.Preferred Attributes : - Strong problem-solving and analytical ability.- Ability to identify underlying architectural, engineering, or data issues rather than addressing only surface-level problems.- Strong communication and stakeholder management skills.- Ability to translate complex technical concepts into clear execution plans.- Strong ownership and bias toward execution.- Comfortable working in a fast-paced, highly collaborative environment.- Strong interest in emerging AI/LLM technologies and their practical application in production systems.Location & Work Environment : - This is an in-office role based in Bengaluru.- Candidates currently based in Bengaluru or willing to relocate are preferred.- The role requires close collaboration with engineering and cross-functional teams.What You Can Expect : - Significant ownership over the technical direction of software and AI systems.- Opportunity to work across full-stack engineering, AI/LLM systems, architecture, and infrastructure.- Direct involvement in building and scaling production-grade AI products.- Opportunity to mentor engineers and influence engineering practices.- A fast-paced environment focused on technical excellence, ownership, and execution. (ref:hirist.tech)