Key Responsibilities :

AI Copilot Solution Architecture and Design :

- Lead the architecture, design, and implementation of enterprise-grade AI Copilot solutions using Large Language Models (LLMs) and Generative AI technologies.

- Design scalable, secure, and reusable AI solution architectures aligned with enterprise standards and business objectives.

- Evaluate emerging AI technologies and recommend appropriate frameworks, tools, and platforms.

- Define technical roadmaps and implementation strategies for AI-powered productivity solutions.

Generative AI and LLM Development :

- Develop intelligent AI assistants and copilots capable of supporting business users, developers, analysts, and operational teams.

- Work with state-of-the-art LLM platforms including :

1. Azure OpenAI

2. OpenAI GPT Models

3. Hugging Face Models

4. IBM Watsonx

- Fine-tune and optimize models for enterprise-specific use cases.

- Develop prompt engineering frameworks to improve response quality, accuracy, and consistency.

- Implement advanced prompting techniques such as :

1. Zero-shot prompting

2. Few-shot prompting

3. Chain-of-thought prompting

4. Role-based prompting

5. Context-aware prompting

AI Copilot Platform Development :

- Build and deploy AI copilots integrated with enterprise productivity platforms such as :

1. Microsoft 365 Copilot

2. GitHub Copilot

3. IBM Watsonx

4. Internal enterprise applications

- Design conversational AI experiences and intelligent workflow automation capabilities.

- Develop reusable AI services and APIs that can be consumed across multiple business functions.

Retrieval-Augmented Generation (RAG) Development :

- Design and implement advanced Retrieval-Augmented Generation (RAG) architectures.

- Develop document ingestion pipelines for structured and unstructured data sources.

- Implement semantic search and knowledge retrieval solutions to improve LLM accuracy.

- Build and optimize :

1. Vector embeddings

2. Similarity search mechanisms

3. Knowledge indexing frameworks

4. Context retrieval systems

- Ensure enterprise knowledge bases are efficiently searchable and consumable by AI applications.

Enterprise Integration :

- Integrate AI copilots with enterprise systems, applications, and APIs.

- Connect AI solutions with :

1. CRM systems

2. ERP applications

3. Knowledge management platforms

4. Collaboration tools

5. Internal databases

- Design microservices-based architectures to enable scalable AI deployments.

- Develop secure APIs and service integrations for real-time AI interactions.

Cloud and AI Infrastructure :

- Deploy and manage AI workloads on cloud platforms, primarily Microsoft Azure.

- Utilize Azure AI services, Azure OpenAI, Azure Machine Learning, and other cloud-native services.

- Design scalable infrastructure capable of supporting high-volume AI interactions.

- Monitor infrastructure performance and optimize resource utilization.

AI Governance, Security, and Compliance :

- Ensure AI solutions comply with enterprise security standards and regulatory requirements.

- Implement Responsible AI principles, including :

1. Fairness

2. Transparency

3. Explainability

4. Privacy

5. Risk mitigation

- Develop governance frameworks for AI model usage and monitoring.

- Ensure secure handling of sensitive enterprise data.

Collaboration and Stakeholder Engagement :

- Collaborate with business stakeholders to identify high-value AI use cases.

- Conduct workshops, requirement-gathering sessions, and solution demonstrations.

- Work closely with architects, developers, data scientists, product managers, and client teams.

- Present AI solutions and technical recommendations to senior leadership and clients.

Leadership and Mentoring :

- Provide technical leadership and guidance to AI engineering teams.

- Mentor junior engineers and support skill development initiatives.

- Establish AI engineering best practices, coding standards, and architectural guidelines.

- Drive innovation and continuous improvement across AI initiatives.

AI Operations and Continuous Improvement :

- Monitor AI model performance, accuracy, and user adoption.

- Establish feedback mechanisms for continuous learning and optimization.

- Manage model lifecycle activities including deployment, versioning, monitoring, retraining, and retirement.

- Troubleshoot production issues and implement performance improvements.

Pre-Sales and Client Support :

- Support proposal development, solution architecture reviews, and client presentations.

- Participate in proof-of-concept (PoC) development and demonstrations.

- Assist sales and consulting teams in showcasing AI capabilities and business value.

Required Technical Skills :

Generative AI and LLMs :

- Strong hands-on experience with :

1. OpenAI GPT Models

2. Azure OpenAI

3. Hugging Face Transformers

4. IBM Watsonx

- Experience designing and deploying enterprise AI assistants and copilots.

- Knowledge of LLM fine-tuning and optimization techniques.

Retrieval-Augmented Generation (RAG) :

- Design and implementation of RAG architectures.

- Semantic search development.

- Embedding generation and vector indexing.

- Knowledge retrieval optimization.

Vector Databases :

- Hands-on experience with vector databases such as :

1. FAISS

2. Pinecone

3. ChromaDB

4. Weaviate

5. Milvus

Natural Language Processing (NLP) :

- Text processing and classification.

- Information extraction.

- Entity recognition.

- Language understanding.

- Conversational AI development.

Programming Languages :

- Python (Mandatory)

- JavaScript/TypeScript (Preferred)

- Experience with :

1. FastAPI

2. Flask

3. LangChain

4. LlamaIndex

5. Semantic Kernel

API and Microservices Development :

- REST API development.

- Microservices architecture.

- API security and authentication.

- Service integration patterns.

Cloud Platforms :

- Strong experience with Microsoft Azure including :

1. Azure OpenAI Service

2. Azure AI Studio

3. Azure Machine Learning

4. Azure Functions

5. Azure Kubernetes Service (AKS)

6. Azure Storage

7. Azure Cognitive Services

DevOps and MLOps :

- CI/CD pipeline implementation.

- Model deployment automation.

- Git-based version control.

- Monitoring and observability frameworks.

Containerization and Orchestration :

- Docker

- Kubernetes

- Container security and deployment strategies.

Security and Governance :

- Data privacy and protection.

- Enterprise security standards.

- Identity and access management.

- AI governance and Responsible AI frameworks.

Required Qualifications :

- Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, Engineering, or a related discipline.

- 911 years of overall software engineering experience.

- Proven experience delivering enterprise-scale AI, cloud, or digital transformation solutions.

- Strong communication, leadership, and stakeholder management skills.

- Ability to work in global and client-facing environments.