LLM Engineer

• Manage and coach engineers across levels L1–L5
• Lead onboarding, performance management, career development, and promotion readiness
• Build a high-trust feedback culture with strong engineering fundamentals, including code review, testing, documentation, and incident learning
• Recruit and hire across multiple levels and scale the team responsibly
• Own delivery outcomes across concurrent projects, including planning, prioritization, scope control, and risk management
• Collaborate with Product, clients, and stakeholders to translate business requirements into technical plans and measurable milestones
• Establish and improve agile operating rhythms, including sprint/kanban, roadmapping, retrospectives, and metrics
• Enforce CI/CD workflows
• Lead implementation and enhancement of conversational AI solutions using Cognigy and/or Amazon Lex + Amazon Q
• Oversee chat and voice bots, routing, integrations, and knowledge-driven experiences
• Ensure best practices for conversational design, NLU/LLM prompt and tooling hygiene, and safe fallbacks or hand-offs to live agents
• Improve bot quality through evaluation, testing, monitoring, and iterative optimization
• Define and enforce standards for reliability, observability, and on-call readiness
• Drive incident response and root-cause reviews
• Partner with security and compliance stakeholders on data handling, retention, and access controls for customer data integrations

Conversational AI Engineering Manager

• Provide hands-on technical leadership across the Process Intelligence Engine squads
• Design, build, test, troubleshoot, review, and ship production-ready software
• Establish engineering patterns, coding standards, integration practices, and quality expectations
• Guide technical decisions and ensure alignment with agreed architecture
• Review implementations and pull requests for correctness, security, scalability, maintainability, and performance
• Design and develop Python-based microservices using FastAPI and REST APIs
• Build cloud-native solutions using AWS services including ECS, S3, DynamoDB, SQS, Secrets Manager, IAM, and ALB
• Develop React and TypeScript components and integrate them with backend services
• Integrate enterprise LLM capabilities through AWS Bedrock / AI Gateway
• Develop LLM-powered workflows using LangGraph, tool and function calling, structured outputs, and workflow orchestration
• Implement semantic and vector search, LLM evaluation, observability, speech processing, and transcript processing where required
• Design asynchronous and event-driven processing using messaging and integration patterns
• Troubleshoot complex issues across applications, integrations, infrastructure, data flows, and AI-enabled services
• Support CI/CD, Docker-based delivery, JFrog artifact management, and SonarQube quality controls
• Mentor engineers and provide technical guidance across squads
• Manage engineering resources, support development planning, and contribute to team capability growth
• Improve delivery quality, service reliability, scalability, maintainability, and operational readiness
• Promote efficient use of cloud compute, storage, networking, and data resources

Tech Chapter Lead – Python, AWS, LLM Engineering, Microservices

• Engineer and deploy production-ready generative AI solutions, including LLMs, VLMs, and multimodal models
• Design and operate LLM Ops pipelines covering model versioning, fine-tuning, evaluation, deployment, rollback, and lifecycle management
• Build and maintain AI platforms and services supporting prompt management, embeddings, vector search, retrieval-augmented generation, and tool-calling workflows
• Integrate generative AI capabilities into enterprise applications using APIs, microservices, and event-driven architectures
• Implement MLOps practices including model CI/CD, automated testing, performance benchmarking, observability, logging, and cost monitoring
• Optimize model latency, throughput, accuracy, and cost using quantization, caching, batching, and model routing
• Collaborate with cloud, data, security, and product teams on enterprise security, governance, and responsible AI standards
• Produce technical documentation and operational runbooks
• Communicate delivery status and business value to stakeholders
• Mentor engineers and contribute to reusable frameworks, standards, and platform capabilities
• Build, deploy, and operate generative AI services across the AI lifecycle, from model onboarding and fine-tuning to inference optimization, monitoring, and continuous improvement

LLM Engineer jobs in India

Conversational AI Engineering Manager

Tech Chapter Lead – Python, AWS, LLM Engineering, Microservices

Lead Generative AI Engineer