Engineering Lead

Job Title: Engineering Lead for Building enterprise scale Gen AI Platform

Location: [India-Offshore]

Experience:11 + Years

Employment Type: [Full-Time / Contract]

Domain: Generative AI, NLP, LLMs, Full-stack Application development

Job Summary:

We are seeking a highly skilled Solution Architect with deep knowledge of full-stack technologies and a strong grasp of LLM-based solution design. The ideal candidate will be responsible for architecting and leading scalable, secure, and high-performing GenAI solutions. This role involves close collaboration with product managers, data scientists, backend developers, and domain teams as well as customer stakeholder to design intelligent systems that integrate advanced language models, knowledge graphs, and cloud-native services. You will provide hands-on technical leadership across the full software development lifecycle—from design to delivery—while setting engineering standards and ensuring alignment with enterprise architecture and compliance frameworks.

Key Responsibilities:

• Architect end-to-end GenAI solutions using LLMs (e.g., OpenAI, Deepseek, Llama) for a variety of enterprise use cases including summarization, semantic search, chat agents, and workflow automation.
• Translate business requirements into scalable LLM-enabled software architectures balancing functional needs with performance, cost, compliance, and user experience requirements.
• Own full-stack solution delivery — from UX/UI design to backend services, data integrations, and LLM orchestration — ensuring cohesive user experiences and seamless data flow across layers.
• Guide development teams on prompt engineering, RAG (retrieval-augmented generation), and LLMOps best practices.
• Implement robust workflow orchestration for GenAI use cases using tools like Temporal, Airflow, or custom orchestrators to support multi-agent workflows and business logic execution.
• [RT1] [RT2] Establish secure and compliant LLM pipelines, ensuring adherence to data protection, PII masking, auditability, and enterprise governance policies.
• Stay abreast of emerging GenAI tools, frameworks, and trends to continuously improve solution design.
• Lead application development lifecycle: define branching strategies, enforce CI/CD practices, and implement automated quality gates (tests, linting, security scans). Architect and implement self-service internal platforms for build, test, deployment, and monitoring.
• Drive engineering best practices including clean code, modularization, reusable component libraries, design patterns for GenAI, and consistent coding standards across teams.
• Maintain architectural oversight across environments, collaborating with DevOps and SRE teams to provision infrastructure using IaC tools (e.g., Terraform, Bicep, Pulumi) and to implement monitoring, logging, and tracing for resilient, observable GenAI platforms.

Required Skills:

• Frontend: , , or similar frameworks.
• Backend: Python, , FastAPI, Flask,
• GenAI Stack: LangChain, LlamaIndex, OpenAI APIs.
• Cloud Platforms: Azure or GCP (LLM deployments, Kubernetes, serverless functions).
• Database: PostgreSQL, familiarity with vector databases.
• Containerization & Orchestration: Docker, Kubernetes, Helm, EKS/GKE/AKS
• CI/CD: GitHub Actions, Jenkins, ArgoCD, FluxCD
• Developer Enablement: Backstage, Internal Dev Portals, Platform APIs
• Experience designing and integrating LLMOps pipelines, prompt templating, and caching strategies.
• Excellent understanding of API architecture (REST, GraphQL).
• Proven experience in agile development methodologies.

Preferred Qualifications:

• Experience working with enterprise-scale product or application development, rollout and adoption
• Hands-on experience with GenAI solution building blocks, LLM fine-tuning, custom embeddings, or proprietary data integration.
• Contributions to open-source AI/NLP projects or participation in GenAI communities.
• Exposure to evaluation frameworks for LLM outputs (toxicity, accuracy, hallucination detection).
• Proven expertise in building internal platforms or PaaS environments.
• Strong understanding of software engineering practices, microservices, and distributed systems.
• Passion for automation, standardization, and developer experience.