Description

Responsibilities:

• Design and architect end-to-end GenAI solutions using LLMs (OpenAI, Azure OpenAI, Google Gemini, Anthropic Claude, etc.)
• Lead the implementation of prompt engineering, RAG (Retrieval-Augmented Generation), fine-tuning, and agentic AI systems
• Define AI architecture patterns, best practices, and governance frameworks
• Build scalable pipelines for data ingestion, vectorization (embeddings), and semantic search
• Collaborate with business stakeholders to identify AI use cases and translate them into technical solutions
• Implement and manage AI model deployment on cloud platforms (Azure, AWS, GCP)
• Ensure responsible AI practices, including security, compliance, and bias mitigation
• Integrate GenAI solutions with enterprise systems (APIs, microservices)
• Mentor engineering teams and drive AI adoption roadmap
• Monitor solution performance and continuously improve accuracy, latency, and cost efficiency Technical Skills

Requirements:

• Strong experience in Python and familiarity with ML/AI frameworks
• Knowledge of containerization (Docker, Kubernetes
• Experience with REST APIs, microservices architecture, and system design
• Familiarity with DevOps/MLOps practices (CI/CD, model monitoring, versioning)
• Hands-on experience with:
• LLM frameworks: LangChain, LlamaIndex, Semantic Kernel
• Vector databases: Pinecone, FAISS, Weaviate, Chroma
• Cloud AI services: Azure OpenAI, AWS Bedrock, Google Vertex AI
• Deep understanding of:
• Transformer architectures and NLP concepts
• Prompt engineering and evaluation techniques
• RAG pipelines and embeddings
• Mandatory skills: Gen AI, Python, AWS, Amazon Q, Kiro, Java, Spring boot, Microservice
• Years of experience required; 12+
• Years of Experience:   10.00 Years of Experience

Key Skills

Education

Any Graduate