Position : Director / Sr. Director - Engineering (GenAI & AI Platform)

Location : Noida, India

Experience : 10+ years in engineering leadership, including 3+ years delivering production GenAI/LLM systems

Industry : Generative AI, Large Language Models, Advanced Analytics, Data Science

The Role :

We are seeking a visionary Director / Sr. Director of Engineering to spearhead our GenAI platform engineering at our India center. You will architect and lead the end-to-end delivery of production-grade GenAI systems - spanning LLM integration, Retrieval-Augmented Generation (RAG) pipelines, autonomous AI agents, multimodal applications, and intelligent automation - embedded directly into Fortune 500 client workflows.

Key Responsibilities :

- Architect and deliver enterprise-grade GenAI platforms - RAG systems, LLM-powered agents, multimodal AI applications, and intelligent automation pipelines at Fortune 500 scale.

- Lead and mentor teams building production LLM pipelines using orchestration frameworks such as LangChain, LlamaIndex, CrewAI, AutoGen, and DSPy.

- Own end-to-end model fine-tuning strategy - including LoRA, QLoRA, RLHF, DPO, and instruction tuning - along with prompt engineering best practices across diverse use cases.

- Design and operate LLM evaluation harnesses (RAGAS, DeepEval, LLM-as-judge) to ensure output quality, factuality, and safety at each release.

- Drive AI safety, responsible AI, and governance frameworks - including hallucination mitigation, bias auditing, prompt injection defense, PII redaction, and regulatory compliance guardrails.

- Integrate GenAI solutions with enterprise data stacks - vector databases, knowledge graphs, structured/unstructured sources - for grounded, context-rich AI experiences.

- Champion LLMOps and MLOps practices: model versioning, observability, cost optimization, CI/CD for AI pipelines, and automated regression testing.

- Partner with global Fortune 500 clients to translate complex business problems into GenAI use cases, technical roadmaps, and measurable ROI.

- Stay at the cutting edge of foundation model releases, open-source LLMs (Llama, Mistral, Gemma, Phi), and emerging AI tooling to continuously enhance delivery.

- Build, inspire, and scale high-performing GenAI engineering teams across India and globally, fostering a culture of innovation, rigor, and rapid iteration.

Ideal Profile :

- 10+ years of engineering leadership; minimum 3 years hands-on building GenAI/LLM systems in production enterprise environments.

- Degree in Computer Science / Engineering from a top-tier institute (IIT / NIT / IISc preferred).

- Deep expertise in LLM orchestration frameworks: LangChain, LlamaIndex, CrewAI, AutoGen, DSPy, Haystack, or equivalent - not just familiarity but shipped-to-production experience.

- Hands-on experience architecting RAG systems and working with vector databases : Pinecone, Weaviate, ChromaDB, Qdrant, pgvector, Milvus.

- Strong Python engineering skills; proficiency integrating Hugging Face Transformers, OpenAI, Anthropic Claude, Google Gemini, AWS Bedrock, or Azure OpenAI APIs at scale.

- Practical experience with model fine-tuning techniques: LoRA, QLoRA, RLHF, DPO, and supervised fine-tuning on custom domain datasets.

- Experience designing and running LLM evaluation pipelines - benchmarking, automated regression testing, and human-in-the-loop feedback loops.

- Proficiency with cloud AI platforms : AWS Bedrock, Azure OpenAI Service, or GCP Vertex AI, including scalable inference infrastructure and cost governance.

- Strong understanding of AI safety principles: hallucination mitigation, prompt injection defense, output filtering, PII handling, and responsible AI governance frameworks.

- Demonstrated success in translating GenAI capabilities into measurable enterprise value - balancing deep technical expertise with executive-level stakeholder communication.

- Experience building agentic AI systems: multi-agent workflows, tool-use, memory architectures, and planning frameworks for autonomous enterprise applications.

- Familiarity with multimodal AI (vision-language models, document AI, audio/speech models) is a strong plus.

- Experience building and scaling globally distributed engineering teams in a fast-paced consulting or product environment.