Gen AI Developer

A fast-growing organisation operating in the Enterprise AI & Generative AI product development sector, building production-grade LLM-powered solutions, retrieval-augmented experiences, and API-driven automation for enterprise customers across domains. Standardized title: Generative AI Engineer. Location: India (on-site).

Role & Responsibilities

• Design, implement, and optimise end-to-end GenAI features: prompt pipelines, embeddings, RAG workflows, and low-latency inference for production services.
• Integrate LLMs and vector search into backend systems using LangChain/transformer-based stacks and vector stores to enable semantic search and contextual generation.
• Develop and maintain scalable Python services and REST APIs that serve inference, embedding, and orchestration workloads.
• Build evaluation and monitoring pipelines for model quality, latency, and drift; implement automated retraining or fallback strategies.
• Collaborate with ML engineers, data scientists, and product teams to productionise models, define SLAs, and drive performance and cost optimisations.
• Contribute to MLOps best practices: containerisation, CI/CD for models, observability, and reproducible model packaging for deployment.

Skills & Qualifications Must-Have

• Python
• PyTorch
• Hugging Face Transformers
• LangChain
• Retrieval-Augmented Generation
• FAISS
• Pinecone
• REST APIs

Preferred

• Docker
• Kubernetes
• AWS SageMaker

• Proven track record of deploying LLM-based services to production and optimising inference throughput and cost.
• Strong understanding of model evaluation, observability, and strategies for handling hallucination and prompt robustness.
• Willingness to work on-site in India and collaborate closely with cross-functional teams to deliver production SLAs.

Benefits & Culture Highlights

• Work directly on cutting-edge GenAI products and influence architecture and model choices end-to-end.
• High-collaboration, fast-paced engineering culture with emphasis on production excellence and measurable impact.
• Competitive compensation, learning budget for AI tooling, and opportunities for technical leadership.