Lead AI Engineer

Job Description 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 Type Script 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 Lang Chain, Lang Graph, and Lang Smith for designing, observing, and optimizing AI workflows.
• Data Storage & Modeling:
  Practical experience with Snowflake (analytics workloads), SQL (schema design & querying), Mongo
  
  DB (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 GCP.
• API Lifecycle Management:
  Experience using Azure APIM for API management, governance, and secure platform integration.
• Infrastructure & Dev Ops Awareness:
  Comfortable with hosting, scaling, CI/CD best practices, authentication/authorization, and secure deployment patterns in enterprise environments.

• 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 trade‑offs.
• 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.

Regards,

Technical Recruiter

Direct: 404‑777‑9837 | Fax: 866‑608‑6686

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