Track Manager - Apache Kafka, Windows PowerShell

Job Summary

We are seeking an experienced and hands-on Lead Engineer - Artificial Intelligence (AI) to design, develop, integrate, and operationalize AI solutions across the organization. The role will focus on translating business and operational requirements into production-ready AI applications, with a strong emphasis on Python engineering, Generative AI, Large Language Models (LLMs), Agentic AI, Machine Learning, Retrieval Augmented Generation (RAG), APIs, and intelligent automation. The successful candidate will work closely with engineering, operations, architecture, security, service management, tooling, data, and business teams to develop scalable and secure AI solutions that improve operational efficiency, automate repetitive activities, enhance decision-making, and improve service delivery. This is a hands-on technical role requiring strong software engineering fundamentals, advanced Python programming skills, practical experience building AI applications, and the ability to technically guide engineers through design and implementation. AI Engineering & Development

• Design, develop, test, and maintain AI-powered applications and services using Python.
• Build solutions leveraging Generative AI, LLMs, Agentic AI, Machine Learning, NLP, RAG, and intelligent automation.
• Develop reusable Python modules, APIs, services, connectors, and AI components.
• Build AI agents capable of interacting with enterprise applications, APIs, databases, knowledge repositories, and automation platforms.
• Develop orchestration workflows integrating AI models with existing enterprise systems and operational processes.
• Apply software engineering best practices including modular design, version control, automated testing, documentation, logging, and error handling.
• Perform code reviews and contribute to engineering standards and reusable development patterns. Solution Design & Implementation
• Translate functional and business requirements into technical designs and implementation approaches.
• Develop Proof of Concepts (PoCs), prototypes and Minimum Viable Products (MVPs) to validate AI use cases.
• Contribute to solution architecture covering application components, APIs, AI models, data sources, vector stores, integrations, authentication, and deployment.
• Collaborate with enterprise architecture, security, infrastructure, cloud, data, and operations teams.
• Support the transition of successful prototypes into scalable production solutions.
• Troubleshoot complex application, integration, model, and performance issues. Python & Software Engineering
• Develop high-quality and maintainable Python applications for AI, automation and integration use cases.
• Build REST APIs and backend services using appropriate Python frameworks.
• Integrate applications with enterprise platforms through REST APIs, SDKs, databases, messaging systems and other interfaces.
• Apply object-oriented programming, asynchronous programming, concurrency and appropriate software design patterns.
• Implement unit testing, integration testing and automated validation.
• Use Git-based development practices including branching, pull requests, code reviews and CI/CD.
• Optimize Python applications for reliability, scalability and performance. AI Operations & Production Readiness
• Support deployment, monitoring, troubleshooting and lifecycle management of AI solutions.
• Implement appropriate application and model observability including logging, tracing, metrics and error handling.
• Develop mechanisms to monitor AI solution quality, latency, usage, reliability and cost.
• Support model and prompt versioning, testing and controlled releases.
• Work with platform and DevOps teams to automate build, deployment and configuration processes.
• Ensure AI solutions are supportable and maintainable within enterprise production environments. Responsible AI, Security & Governance
• Implement AI solutions in accordance with organizational security, privacy, compliance and Responsible AI

Key Responsibilities

We are seeking an experienced and hands-on Lead Engineer - Artificial Intelligence (AI) to design, develop, integrate, and operationalize AI solutions across the organization. The role will focus on translating business and operational requirements into production-ready AI applications, with a strong emphasis on Python engineering, Generative AI, Large Language Models (LLMs), Agentic AI, Machine Learning, Retrieval Augmented Generation (RAG), APIs, and intelligent automation. The successful candidate will work closely with engineering, operations, architecture, security, service management, tooling, data, and business teams to develop scalable and secure AI solutions that improve operational efficiency, automate repetitive activities, enhance decision-making, and improve service delivery. This is a hands-on technical role requiring strong software engineering fundamentals, advanced Python programming skills, practical experience building AI applications, and the ability to technically guide engineers through design and implementation. AI Engineering & Development

• Design, develop, test, and maintain AI-powered applications and services using Python.
• Build solutions leveraging Generative AI, LLMs, Agentic AI, Machine Learning, NLP, RAG, and intelligent automation.
• Develop reusable Python modules, APIs, services, connectors, and AI components.
• Build AI agents capable of interacting with enterprise applications, APIs, databases, knowledge repositories, and automation platforms.
• Develop orchestration workflows integrating AI models with existing enterprise systems and operational processes.
• Apply software engineering best practices including modular design, version control, automated testing, documentation, logging, and error handling.
• Perform code reviews and contribute to engineering standards and reusable development patterns. Solution Design & Implementation
• Translate functional and business requirements into technical designs and implementation approaches.
• Develop Proof of Concepts (PoCs), prototypes and Minimum Viable Products (MVPs) to validate AI use cases.
• Contribute to solution architecture covering application components, APIs, AI models, data sources, vector stores, integrations, authentication, and deployment.
• Collaborate with enterprise architecture, security, infrastructure, cloud, data, and operations teams.
• Support the transition of successful prototypes into scalable production solutions.
• Troubleshoot complex application, integration, model, and performance issues. Python & Software Engineering
• Develop high-quality and maintainable Python applications for AI, automation and integration use cases.
• Build REST APIs and backend services using appropriate Python frameworks.
• Integrate applications with enterprise platforms through REST APIs, SDKs, databases, messaging systems and other interfaces.
• Apply object-oriented programming, asynchronous programming, concurrency and appropriate software design patterns.
• Implement unit testing, integration testing and automated validation.
• Use Git-based development practices including branching, pull requests, code reviews and CI/CD.
• Optimize Python applications for reliability, scalability and performance. AI Operations & Production Readiness
• Support deployment, monitoring, troubleshooting and lifecycle management of AI solutions.
• Implement appropriate application and model observability including logging, tracing, metrics and error handling.
• Develop mechanisms to monitor AI solution quality, latency, usage, reliability and cost.
• Support model and prompt versioning, testing and controlled releases.
• Work with platform and DevOps teams to automate build, deployment and configuration processes.
• Ensure AI solutions are supportable and maintainable within enterprise production environments. Responsible AI, Security & Governance
• Implement AI solutions in accordance with organizational security, privacy, compliance and Responsible AI

Skill Requirements

Mandatory Technical Skills Python Strong hands-on experience with Python is essential, including:

• Object-oriented programming
• Data structures and algorithms
• REST API development and integration
• Asynchronous programming
• Error handling and logging
• Unit and integration testing
• Package and dependency management
• Code optimization
• Software design patterns
• Git-based development Experience with commonly used Python libraries and frameworks such as:
• FastAPI / Flask
• Pydantic
• Pandas / NumPy
• PyTest
• AI/ML and LLM SDKs/frameworks as appropriate Generative AI & LLMs Hands-on knowledge of:
• Large Language Models
• Prompt engineering
• Structured outputs and tool/function calling
• Retrieval Augmented Generation (RAG)
• Embeddings
• Vector databases
• Semantic search
• Context management
• Model evaluation
• LLM integration patterns Experience with platforms/models such as:
• Azure OpenAI / OpenAI
• Anthropic
• Google Gemini
• Open-source LLMs Agentic AI Practical understanding of:
• AI agents
• Tool/API-enabled agents
• Agent orchestration
• Multi-step AI workflows
• Agent memory and context
• Human-in-the-loop workflows
• Guardrails and controlled execution Experience with relevant agentic frameworks or SDKs is advantageous.

Other Requirements

Mandatory Technical Skills Python Strong hands-on experience with Python is essential, including:

• Object-oriented programming
• Data structures and algorithms
• REST API development and integration
• Asynchronous programming
• Error handling and logging
• Unit and integration testing
• Package and dependency management
• Code optimization
• Software design patterns
• Git-based development Experience with commonly used Python libraries and frameworks such as:
• FastAPI / Flask
• Pydantic
• Pandas / NumPy
• PyTest
• AI/ML and LLM SDKs/frameworks as appropriate Generative AI & LLMs Hands-on knowledge of:
• Large Language Models
• Prompt engineering
• Structured outputs and tool/function calling
• Retrieval Augmented Generation (RAG)
• Embeddings
• Vector databases
• Semantic search
• Context management
• Model evaluation
• LLM integration patterns Experience with platforms/models such as:
• Azure OpenAI / OpenAI
• Anthropic
• Google Gemini
• Open-source LLMs Agentic AI Practical understanding of:
• AI agents
• Tool/API-enabled agents
• Agent orchestration
• Multi-step AI workflows
• Agent memory and context
• Human-in-the-loop workflows
• Guardrails and controlled execution Experience with relevant agentic frameworks or SDKs is advantageous.