Generative AI Developer / Technical Lead / Architect

Hiring: Generative AI Developer / Technical Lead / Architect

Experience: 511 Years
Skills: Generative AI, LLM, Python, RAG, Prompt Engineering
Job Type: Full-Time

Job Summary

We are looking for experienced Generative AI professionals with strong expertise in Python, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and Prompt Engineering. Candidates should have hands-on experience in designing, developing, deploying, and optimizing GenAI applications and enterprise-grade AI solutions.

We are hiring across Developer, Technical Lead, and Architect levels based on technical expertise and leadership experience.

Key Responsibilities

• Design, develop, and implement Generative AI applications using Python and Large Language Models (LLMs).
• Build and optimize RAG pipelines for enterprise knowledge retrieval and context-aware response generation.
• Develop effective prompts, prompt templates, and advanced prompt engineering strategies.
• Work with LLMs such as GPT, Llama, Claude, Gemini, or other open-source and commercial models.
• Implement document ingestion, text chunking, embedding generation, semantic search, and vector database integrations.
• Integrate GenAI solutions with enterprise applications, APIs, and backend systems.
• Evaluate model performance, response quality, hallucinations, latency, and token usage.
• Implement security, data privacy, guardrails, and responsible AI practices.
• Deploy and monitor GenAI applications in cloud or production environments.
• Collaborate with product managers, data scientists, engineering teams, and business stakeholders.

Mandatory Skills

• Strong hands-on experience in Python programming.
• Practical experience with Generative AI and LLM-based applications.
• Strong knowledge of Retrieval-Augmented Generation (RAG).
• Expertise in Prompt Engineering and LLM response optimization.
• Experience with embeddings, semantic search, and vector databases.
• Knowledge of REST APIs, backend development, and AI application integration.
• Understanding of LLM evaluation, hallucination mitigation, and model optimization.

Preferred Skills

• Frameworks: LangChain, LlamaIndex, LangGraph.
• Vector databases: FAISS, Pinecone, Chroma, Weaviate, or Azure AI Search.
• Cloud platforms: AWS Bedrock, Azure OpenAI, or Google Vertex AI.
• Knowledge of fine-tuning, Hugging Face Transformers, and open-source LLMs.
• Experience with agentic AI, AI agents, tool calling, and multi-agent workflows.
• Familiarity with Docker, Kubernetes, CI/CD, and MLOps/LLMOps.

Role-Specific Expectations

1. Generative AI Developer

• Hands-on coding and development of LLM-powered applications.
• Build RAG workflows, API integrations, and prompt templates.
• Debug, test, and optimize GenAI solutions.

2. Generative AI Technical Lead

• Lead technical implementation and guide development teams.
• Review code, establish best practices, and drive solution delivery.
• Optimize architecture, performance, scalability, and reliability.

3. Generative AI Architect

• Define end-to-end GenAI solution architecture and technology choices.
• Design scalable, secure, enterprise-grade RAG and LLM platforms.
• Drive architecture standards, governance, integration strategies, and technical roadmaps.

Experience Criteria

• 5–7 Years: Senior GenAI Developer
• 7–9 Years: GenAI Developer / Technical Lead
• 9–11 Years: Technical Lead / GenAI Architect

Candidates must demonstrate relevant hands-on experience in Python, LLMs, RAG, and Prompt Engineering. Leadership and architecture responsibilities will depend on the position.

Keywords: Generative AI, GenAI, LLM, Large Language Models, Python, RAG, Retrieval-Augmented Generation, Prompt Engineering, LangChain, LlamaIndex, LangGraph, Vector Database, Embeddings, Semantic Search, GPT, Azure OpenAI, AWS Bedrock, Vertex AI, Agentic AI.