Senior AI Developer with Agentic AI, Telecom

Role: Senior AI Developer with Agentic AI, Telecom

Location: Remote

Duration: Long term

Here is the JD for new role for our turnekey solutions AI project

This is for an Agentic Workflow Solution we are building around TDM Circuit Disconnect Validation.  This will be the hands on lead developer with a small team.  He or she must be primarily a rock solid developer, with that being their primary focus, but needs to be able to understand design and architecture.  All of the data is around legacy TDM circuits and related information, so an experience working with Telecom networks, network data or related systems or that data is HIGHLY beneficial. 

Summary:

• We are seeking a hands-on Sr. AI Solutions Developer experienced in building, orchestrating, and deploying agent-based AI systems leveraging Agentic Workflow architectures, large language models (LLMs), reinforcement learning through human feedback mechanisms, vector databases and multiple data sources
• This role requires strong technical depth in Python development, Azure AI services, and multi-agent orchestration frameworks to design and deliver intelligent, context-aware AI systems.
• The Sr. Developer will serve as a key technical resource, translating solution blueprints into working, scalable AI applications—bridging the gap between architecture and production-grade implementation.

Key Responsibilities:

Hands-On Development:

• Design, code, and deploy agentic AI workflows using Azure OpenAI, Azure Cognitive Services, Azure ML, and multiple LLMs (OpenAI, Anthropic Claude, Gemini, etc.), and reinforcement learning models.

Agent & Context Orchestration:

• Develop and manage intelligent multi-agent systems that collaborate across dynamic workflows, ensuring contextual continuity and autonomy potentially through MCP integrations.

Full Lifecycle Ownership:

• Lead the full solution delivery process—prototyping, integrating, testing, deploying, and optimizing agent-based AI systems with measurable business outcomes.

Framework & Tooling Development:

• Build modular and reusable components, SDKs, and internal tools for AI applications, integrating vector databases, reinforcement learning models, knowledge stores, and API or MCP connectors.

Data & Context Management:

• Implement strategies for structured/unstructured data ingestion, contextual retrieval, and memory persistence to improve reasoning and decision-making across agentic systems.

LLM & Model Integration:

Tune prompts, manage context windows, and integrate multiple model endpoints for task-specific intelligence across the agent network.

Continuous Optimization:

• Monitor system performance, trace agent behavior, and apply telemetry and logging to refine accuracy, latency, and cost efficiency.

Collaboration & Leadership:

Work closely with AI architect, MLOps engineers, and data engineer to align architecture and delivery.

Documentation & Communication:

• Produce detailed technical documentation, workflow diagrams, and codebase guidelines for ongoing scalability and knowledge sharing.

Qualifications:

Required:

• 8–10 years of professional software development experience, including 4+ years building AI or ML-driven applications.
• Proven hands-on expertise implementing agentic AI systems using Azure AI, OpenAI API, or similar LLM providers (Anthropic, Gemini, etc.).
• Strong proficiency in Python, including experience with FastAPI, LangChain, LangGraph, semantic caching, or vector database frameworks (e.g., Pinecone, Chroma, Weaviate).
• Deep working knowledge of context-driven architecture for managing conversational and operational state across agents.
• 2-3 years of experience building AI applications with RLHF model capabilities
• Experience deploying production-grade AI workflows in Azure environments, leveraging CI/CD, containerization (Docker/Kubernetes), and Azure DevOps pipelines.
• Ability to integrate and manage multi-agent orchestration, message queues, and API-driven microservices.
• Strong understanding of AI security, RBAC, and compliance in enterprise or regulated environments.
• Excellent communication skills with cross-functional teams, including product managers, data engineers, and executives.

Preferred:

• Azure AI Engineer Associate or Azure Developer Associate certification.
• Experience operationalizing MCP-based agentic frameworks or enterprise-scale AI copilots.
• Familiarity with MLOps/DevOps, prompt optimization, and observability tools for AI systems.
• Exposure to Responsible AI and ethical agent governance frameworks.