AI Engineer

1. AI Use Case Development

a. Collaborate with business teams to identify high-impact AI use cases.

b. Design and implement AI workflows using LLMs and architectures such as RAG, tools-based agents, or prompt chains.

c. Prototype and deliver solutions using tools like OpenAI, Azure OpenAI, Claude, Gemini, or similar.

2. Solution Architecture & Integration

a. Apply modern AI solution patterns (e.g., simple LLM applications, RAG pipelines, or multi-agent frameworks).

b. Build APIs, middleware, or front-end applications to integrate AI capabilities into business systems.

c. Leverage vector databases (e.g., Pinecone, FAISS, Weaviate) for semantic search and retrieval.

d. Design scalable solutions aligned with enterprise architecture principles and cloud best practices.

3. Full-Stack Development

a. Develop front-end applications (e.g., React, Next.js, or similar) for delivering AI-powered user experiences.

b. Implement back-end services (e.g., Node.js, Python, FastAPI) for orchestrating AI pipelines and integrations.

c. Ensure secure authentication, role-based access, and seamless UX for AI-powered tools.

4. Prompt Engineering & Tuning

a. Design, test, and optimize prompts to guide LLM behavior effectively.

b. Implement few-shot, chain-of-thought, or function-calling techniques to enhance performance.

5. Orchestration & Automation

a. Use orchestration frameworks like LangChain, LlamaIndex, Semantic Kernel, or similar.

b. Create modular pipelines for composable AI development using open-source and enterprise tools.

6. Testing, Evaluation & Monitoring

a. Evaluate LLM-based systems using qualitative and quantitative metrics (e.g., accuracy, hallucination rate, latency).

b. Monitor performance in production and implement improvements based on user feedback or business outcomes.

7. Cloud Infrastructure & Deployment

a. Deploy AI systems on cloud platforms (e.g., AWS, Azure, GCP).

b. Use containerization and orchestration (Docker, Kubernetes) for scalable and resilient services.

c. Apply DevOps/MLOps practices for CI/CD, observability, and ongoing system reliability.

8. Documentation & Best Practices

a. Document AI workflows, data flows, and integration points.

b. Promote reusable components, prompt templates, and architecture patterns.

c. Share learnings, standards, and guidelines across the organization.

Skills

1. AI Use Case Development

a. Collaborate with business teams to identify high-impact AI use cases.

b. Design and implement AI workflows using LLMs and architectures such as RAG, tools-based agents, or prompt chains.

c. Prototype and deliver solutions using tools like OpenAI, Azure OpenAI, Claude, Gemini, or similar.

2. Solution Architecture & Integration

a. Apply modern AI solution patterns (e.g., simple LLM applications, RAG pipelines, or multi-agent frameworks).

b. Build APIs, middleware, or front-end applications to integrate AI capabilities into business systems.

c. Leverage vector databases (e.g., Pinecone, FAISS, Weaviate) for semantic search and retrieval.

d. Design scalable solutions aligned with enterprise architecture principles and cloud best practices.

3. Full-Stack Development

a. Develop front-end applications (e.g., React, Next.js, or similar) for delivering AI-powered user experiences.

b. Implement back-end services (e.g., Node.js, Python, FastAPI) for orchestrating AI pipelines and integrations.

c. Ensure secure authentication, role-based access, and seamless UX for AI-powered tools.

4. Prompt Engineering & Tuning

a. Design, test, and optimize prompts to guide LLM behavior effectively.

b. Implement few-shot, chain-of-thought, or function-calling techniques to enhance performance.

5. Orchestration & Automation

a. Use orchestration frameworks like LangChain, LlamaIndex, Semantic Kernel, or similar.

b. Create modular pipelines for composable AI development using open-source and enterprise tools.

6. Testing, Evaluation & Monitoring

a. Evaluate LLM-based systems using qualitative and quantitative metrics (e.g., accuracy, hallucination rate, latency).

b. Monitor performance in production and implement improvements based on user feedback or business outcomes.

7. Cloud Infrastructure & Deployment

a. Deploy AI systems on cloud platforms (e.g., AWS, Azure, GCP).

b. Use containerization and orchestration (Docker, Kubernetes) for scalable and resilient services.

c. Apply DevOps/MLOps practices for CI/CD, observability, and ongoing system reliability.

8. Documentation & Best Practices

a. Document AI workflows, data flows, and integration points.

b. Promote reusable components, prompt templates, and architecture patterns.

c. Share learnings, standards, and guidelines across the organization.