AI Engineer with .NET + Angular Experience

Position: AI Engineer with .Net + angular Location: Dallas, TX (3 days work from office) Duration: Long Term Visa: H1B, & USC   Must have skills: Python integrate with API development skills Agentic ai Data Storage & Modeling: AWS (no azure) Angular .NET Job Description   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. 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.