Job Description :

We are looking for an experienced GenAI Technical Lead to design and lead the development of enterprise Generative AI solutions. The role requires strong hands-on expertise in Python, microservices, LLMs, RAG, agent-based applications, prompt engineering, and enterprise AI architecture, along with the ability to translate business requirements into scalable and secure technical solutions.

The ideal candidate will have experience taking GenAI solutions from architecture and proof of concept through production deployment, with a strong focus on performance, security, cost optimization, and enterprise integration.

Key Responsibilities :

- Lead the design and development of enterprise Generative AI applications using LLMs and modern AI application architectures.

- Design and integrate GenAI capabilities with Python-based microservices and enterprise applications.

- Build and deploy RAG-based and agent-based applications using appropriate frameworks, models, retrieval mechanisms, and orchestration patterns.

- Evaluate and integrate enterprise LLM APIs and locally deployed/open-source language models based on business, security, performance, and cost requirements.

- Design high-level and low-level technical solutions, including HLDs, LLDs, architecture diagrams, and sequence diagrams.

- Develop effective prompt engineering strategies and optimize prompts for accuracy, reliability, context utilization, and cost.

- Optimize LLM applications for cost, latency, throughput, context-window utilization, and response quality.

- Design secure GenAI solutions with appropriate security, privacy, authentication, authorization, access controls, and data protection mechanisms.

- Establish appropriate guardrails and controls for enterprise GenAI applications, including protection of sensitive data and responsible use of LLMs.

- Work closely with product managers, architects, engineering teams, and business stakeholders to translate requirements into scalable GenAI solutions.

- Lead technical discussions, conduct design and code reviews, and provide technical guidance to engineering teams.

- Support the evaluation, prototyping, deployment, monitoring, and continuous improvement of GenAI solutions.

- Troubleshoot application, integration, model, performance, and production issues.

- Stay current with developments in LLMs, RAG, AI agents, model serving, GenAI frameworks, and enterprise AI engineering.

Requirements :

- 4 - 9 years of experience in software engineering, AI/ML engineering, or related technical roles, with strong hands-on experience in Generative AI.

- Strong programming experience in Python and development of production-grade microservices.

- Hands-on experience building RAG and/or agent-based GenAI applications.

- Strong understanding of LLMs, embeddings, vector databases, retrieval techniques, prompt engineering, and LLM application architecture.

- Experience working with enterprise LLM APIs such as OpenAI, Azure OpenAI, Anthropic, Gemini, or equivalent, and/or locally deployed open-source models.

- Experience with GenAI/LLM frameworks such as LangChain, LlamaIndex, Semantic Kernel, or equivalent.

- Strong understanding of REST APIs, microservices, API integration, and distributed application architectures.

- Experience designing HLDs, LLDs, architecture diagrams, and sequence diagrams.

- Hands-on experience optimizing LLM applications for cost, latency, throughput, context utilization, and response quality.

- Strong understanding of enterprise security, privacy, authentication, authorization, access control, and data protection requirements for GenAI applications.

- Experience deploying and supporting GenAI applications in cloud or enterprise environments.

- Good understanding of software engineering practices including Git, CI/CD, testing, code reviews, and production support.

- Strong problem-solving, communication, stakeholder management, and technical leadership skills.

- Ability to mentor engineers and drive engineering best practices across GenAI development.