AI Engineer with .NET + Angular Experience

Must have skills:

Python integrate with API development skills

Agentic ai

Data Storage & Modeling:

AWS (no azure)

Angular
.NET

Job Description

• .NET/C# Systems Integration: Skilled in developing enterprise-grade APIs and service layers using .NET/C#, including secure integration with internal platforms.
• Front-End Development: Proficient with Angular and TypeScript for building modern, scalable, and user-friendly front-end applications.
• AI & LLM Engineering: Hands-on experience with LLMs, prompt engineering, vector search, embeddings, evaluation techniques, and multi-step workflows.
• Agentic Architectures: Familiar with enterprise agent frameworks and tools such as LangChain, LangGraph, and LangSmith for designing, observing, and optimizing AI workflows.
• Data Storage & Modeling: Practical experience with Snowflake (analytics workloads), SQL (schema design & querying), MongoDB (document storage), and Neo4j (graph reasoning).
• Event Streaming: Knowledge of Kafka for event-driven communication and real-time system processing.
• Cloud Deployment: Capable of designing and deploying full-stack AI solutions across AWS (primary), with additional exposure to Azure and Google Cloud Platform.
• API Lifecycle Management: Experience using Azure APIM for API management, governance, and secure platform integration.
• Infrastructure & DevOps Awareness: Comfortable with hosting, scaling, CI/CD best practices, authentication/authorization, and secure deployment patterns in enterprise environments.
• Soft Skills:
• End-to-End Ownership: Able to independently architect, implement, and deliver complete AI-driven applications across the full vertical stack.
• Product & Technical Communication: Communicates clearly, proactively, and respectfully; provides technical recommendations with clear options and tradeoffs.
• Cross-Functional Collaboration: Works effectively with product, engineering, and business stakeholders; listens deeply before shaping solutions.
• Consultative Mindset: Brings clarity in ambiguous situations, offers guidance, and helps drive decision-making with well-reasoned technical insight.
• Adaptability: Quickly ramps up in unfamiliar domains and remains effective amid evolving priorities or strategic pivots.
• Knowledge Sharing: Elevates internal engineering teams by openly sharing expertise in AI systems, data modeling, cloud patterns, and platform best practices.
• Quality & Velocity: Delivers tangible outcomes quickly while maintaining a high standard of engineering quality, reliability, and maintainability.
• Direct & Constructive Dialogue: Comfortable giving and receiving candid feedback and iterating to improve solutions, processes, and team cohesion.

Roles & Responsibilities

• Technical Skills:
• Python Engineering: Strong experience building backend services, AI workloads, orchestration layers, and agentic systems using Python.
• .NET/C# Systems Integration: Skilled in developing enterprise-grade APIs and service layers using .NET/C#, including secure integration with internal platforms.
• Front-End Development: Proficient with Angular and TypeScript for building modern, scalable, and user-friendly front-end applications.
• AI & LLM Engineering: Hands-on experience with LLMs, prompt engineering, vector search, embeddings, evaluation techniques, and multi-step workflows.
• Agentic Architectures: Familiar with enterprise agent frameworks and tools such as LangChain, LangGraph, and LangSmith for designing, observing, and optimizing AI workflows.
• Data Storage & Modeling: Practical experience with Snowflake (analytics workloads), SQL (schema design & querying), MongoDB (document storage), and Neo4j (graph reasoning).
• Event Streaming: Knowledge of Kafka for event-driven communication and real-time system processing.
• Cloud Deployment: Capable of designing and deploying full-stack AI solutions across AWS (primary), with additional exposure to Azure and Google Cloud Platform.
• API Lifecycle Management: Experience using Azure APIM for API management, governance, and secure platform integration.
• Infrastructure & DevOps Awareness: Comfortable with hosting, scaling, CI/CD best practices, authentication/authorization, and secure deployment patterns in enterprise environments.
• Soft Skills:
• End-to-End Ownership: Able to independently architect, implement, and deliver complete AI-driven applications across the full vertical stack.
• Product & Technical Communication: Communicates clearly, proactively, and respectfully; provides technical recommendations with clear options and tradeoffs.
• Cross-Functional Collaboration: Works effectively with product, engineering, and business stakeholders; listens deeply before shaping solutions.
• Consultative Mindset: Brings clarity in ambiguous situations, offers guidance, and helps drive decision-making with well-reasoned technical insight.
• Adaptability: Quickly ramps up in unfamiliar domains and remains effective amid evolving priorities or strategic pivots.
• Knowledge Sharing: Elevates internal engineering teams by openly sharing expertise in AI systems, data modeling, cloud patterns, and platform best practices.
• Quality & Velocity: Delivers tangible outcomes quickly while maintaining a high standard of engineering quality, reliability, and maintainability.

Know anyone who might be interested?
Kindly let me know if you have any Suitable Candidates available for this opportunity.
📧
📱 +1

• Direct & Constructive Dialogue: Comfortable giving and receiving candid feedback and iterating to improve solutions, processes, and team cohesion.