- Senior professional with around 10 years of experience.
- 3+ years of relevant experience in AI Engineering
- Working experience in an agile software development environment with a good understanding of the principles of agile architecture. Strong collaborative mindset for
collective decentralized decision making.
- Demonstrate strong technical skills with a deep understanding of modern architectural styles and practices such as Microservices, Containers, Cloud (AWS, Azure), APIs, Continuous Delivery, Event-driven architecture, Evolutionary architecture, etc., with a passion for hands on coding.
- Strong foundation knowledge of software architecture concepts, patterns, principles, and quality attributes. Ability to consistently apply them in real-world scenarios with a pragmatic, system thinking, and problem-solving mindset by analyzing architecture trade-offs for delivering high-quality, sustainable solution architecture.
- Proven leadership skills with a proactive, positive, and growth mindset. Ability to foster and motivate programmers for delivering with craftsmanship. Good personal skills to continuously engage and communicate with an egoless empathetic mindset.
- Experience and expertise in delivering architecture for large software solutions meeting critical business purposes. Ability to proactively discover technical debts and continuous improvement opportunities of existing live systems. Work closely with the product owner and enterprise architects to influence and prioritize technical backlog items.
- Strong proficiency in Python (NumPy, pandas, FastAPI, PyTorch/TensorFlow).
- Experience with Agent framework LangChain, Lang Graph.
- Architect end-to-end data indexing pipelines optimized for semantic search and RAG.
- Design resilient data ingestion frameworks that ensure high availability and low latency for vectorized datasets.
- Experience deploying and managing models via Amazon Bedrock, Azure OpenAI Service, and Google Vertex AI.
- Integration of pre-built AI APIs for Speech-to-Text, Computer Vision, and Natural Language Processing (NLP).
- Leveraging serverless architectures (e.g., AWS Lambda, Azure Functions) to trigger AI inference and data pre-processing.
- Experience working with CI/CD tools like Jenkins/Gitlab in Cloud Native environments.
- Experience in setting up pipeline for cloud based and on-prem based application with static code analysis, requirement tagging in Jira.
- Experience working with one or more DevOps tools & config management tools.
- Experience in managing and deploying AI workloads in Kubernetes.
- Experience operating monitoring tools for traditional and cloud environments.
- Strong analytical mind for problem solving.
