Senior AI Engineer - Deep Learning Frameworks

Sr AI Engineer :

- 8-10 years of experience in software development.

- 3+ years of hands on experience delivering AI/ML solutions in production environments.

- Proven experience building and deploying generative AI solutions using OpenAI compatible APIs.

- Demonstrated ability to lead end to end delivery of AI powered solutions across cross functional teams.

Technical Skills :

- Advanced proficiency in Python for AI/ML development, experimentation, and automation.

- Strong experience with deep learning frameworks such as PyTorch, TensorFlow, and Scikit Learn.

- Solid understanding of RAG (Retrieval Augmented Generation) architectures, including vector databases and semantic search.

- Hands on experience with Azure OpenAI, LLM Gateway, Azure Functions, Azure Monitor, and Application Insights.

- Experience with cloud based AI and data services, including Databricks.

- Expertise in Azure Document Intelligence, OCR pipelines, and enterprise document processing.

- Experience or knowledge of agentic AI frameworks, AI Foundry patterns, LangFuse style observability, and enterprise search.

- Familiarity with NLP, Speech AI, Vision AI, and both supervised and unsupervised machine learning techniques.

- Experience with GitHub and M365 Copilot in modern development workflows.

Preferred Qualifications :

- Experience publishing research or contributing to open source AI initiatives.

- Experience designing AI systems for regulated or enterprise environments.

- Strong understanding of AI governance, responsible AI practices, and compliance frameworks.

- Prior experience mentoring technical teams or leading research initiatives.

AI Skills :

- All contractor resources are expected to demonstrate baseline proficiency in enterprise-approved AI tools as part of their day-to-day responsibilities. This includes, but is not limited to :

1. Consistent Use: Maintain a minimum of 90% weekly usage of AI tools such as GitHub Copilot, Microsoft 365 Copilot, and other GenAI platforms approved by the enterprise.

2. Applied Productivity: Leverage AI tools to enhance coding, documentation, data analysis, and decision-making workflows.

3. Continuous Learning: Stay current with evolving AI capabilities and features and apply them to improve delivery quality and velocity.

(ref:hirist.tech)