Description : Role Summary :
We are seeking an experienced Generative AI Developer / Architect with a strong track record of building and deploying LLM-powered applications in real-world environments. The ideal candidate will bring deep technical expertise in LLMs, prompt engineering, RAG architectures, and cloud-based AI services (Azure OpenAI, AWS Bedrock).
This is a high-impact role for someone who thrives at the intersection of innovation, scale, and responsible AI. Youll be driving the end-to-end design, development, and deployment of production-grade GenAI solutions for diverse enterprise use cases.
Key Responsibilities :
- Design and develop GenAI applications using state-of-the-art LLMs such as GPT (OpenAI / Azure), Claude (Anthropic), LLaMA (Meta), etc.
- Build prompt engineering pipelines, including few-shot, chain-of-thought, and role-based prompts for enhanced context understanding.
- Develop RAG (Retrieval-Augmented Generation) pipelines using vector databases for dynamic knowledge retrieval from enterprise data.
- Architect and implement agent-based GenAI workflows for multi-step tasks, automation, and decision-making systems.
- Deploy scalable GenAI applications on Azure OpenAI (AI Studio) or AWS Bedrock, leveraging managed services and serverless infrastructure.
- Utilize Python, FastAPI, and frameworks like LangChain, LLamaIndex, or Haystack for backend orchestration.
- Integrate vector databases such as FAISS, Pinecone, Weaviate, or Chroma for efficient embedding storage and similarity search.
- Implement cost optimization strategies by identifying high-impact GenAI use cases and minimizing token consumption.
- Enforce Responsible AI practices, including fairness, explainability, and privacy compliance across all GenAI deployments.
- Design controls for prompt injection prevention, jailbreaking mitigation, and output filtering using input / output sanitization.
- Build enterprise Q&A systems using embedded knowledge bases, PDFs, internal wikis, and structured databases.
- Implement Human-in-the-Loop (HITL) mechanisms for validation, continuous learning, and human oversight.
- Design multimodal pipelines (text + image + voice) and handle real-time parsing, transcription, chunking, and token management.
Required Skills & Experience :
9 to 15 years of total experience, with minimum 23 years in LLM / GenAI development and real-world solution delivery.Hands-on experience with Azure OpenAI, AWS Bedrock, or other cloud-based GenAI offerings.Proficiency in Python, with strong backend development skills using FastAPI, Flask, or Django.Deep understanding of LLM operations, prompt engineering, and optimization for latency and cost.Experience with LangChain, LLamaIndex, Transformers (Hugging Face), and embedding models (e.g., OpenAI, Cohere, Azure Embeddings).Hands-on experience with Vector Databases like Pinecone, FAISS, Weaviate, or Qdrant.Familiarity with embedding techniques, document chunking, and similarity scoring algorithms.Exposure to prompt safety, Responsible AI frameworks, and data privacy regulations (e.g., GDPR, HIPAA).Experience deploying GenAI apps in production environments, with a strong understanding of MLOps and CI / CD pipelines for LLM applications.(ref : hirist.tech)