About the role
- Work on solutions using RAG, vector databases, embeddings, prompt engineering, and LLM orchestration frameworks.
- Develop and integrate REST APIs, microservices, and cloud-based services to support AI applications.
- Work closely with business stakeholders, product teams, data scientists, ML engineers, and developers to translate requirements into technical solutions.
- Implement best practices around AI security, data privacy, responsible AI, model governance, scalability, and performance.
- Support deployment and monitoring of GenAI solutions using cloud platforms, CI/CD, DevOps, and MLOps practices.
Basic Qualifications:
- Bachelor's degree in Computer Science, Information Technology, Engineering, or related field. 6–8 years of experience in software, cloud, or solution architecture.
- Strong hands-on experience with Generative AI, LLMs, and AI application development.
- Experience with RAG, prompt engineering, embeddings, vector databases, and LLM integration.
- Proficiency in Python/ Java and API/microservices development. Experience with at least one major cloud platform: AWS (Preferred), Azure, or GCP.
- Experience with at least one major cloud platform: AWS (Preferred), Azure, or GCP.
- Experience with technologies such as Azure OpenAI, OpenAI, AWS Bedrock, Vertex AI, LangChain, or LlamaIndex.
- Strong understanding of cloud architecture, distributed systems, APIs, security, and scalability.
- Excellent communication, technical leadership, and stakeholder-management skills.
Preferred Qualifications:
- Experience building AI agents / Agentic AI solutions.
- Knowledge of MLOps/LLMOps and AI observability.
- Experience with Docker, Kubernetes, Terraform, and CI/CD.
- Experience with Vector databases.