Technical Lead - AI & Engineering

Tech Lead - Engineering & AI

About 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.