AI Engineer

Key Responsibilities:

1. AI Use Case Development
2. Collaborate with business teams to identify high-impact AI use cases.
3. Design and implement AI workflows using LLMs and architectures such as RAG, tools-based agents, or prompt chains.
4. Prototype and deliver solutions using tools like OpenAI, Azure OpenAI, Claude, Gemini, or similar.
5. Solution Architecture & Integration
6. Apply modern AI solution patterns (e.g., simple LLM applications, RAG pipelines, or multi-agent frameworks).
7. Build APIs, middleware, or front-end applications to integrate AI capabilities into business systems.
8. Leverage vector databases (e.g., Pinecone, FAISS, Weaviate) for semantic search and retrieval.
9. Design scalable solutions aligned with enterprise architecture principles and cloud best practices.
10. Full-Stack Development
11. Develop front-end applications (e.g., React, Next.js, or similar) for delivering AI-powered user experiences.
12. Implement back-end services (e.g., Node.js, Python, FastAPI) for orchestrating AI pipelines and integrations.
13. Ensure secure authentication, role-based access, and seamless UX for AI-powered tools.
14. Prompt Engineering & Tuning
15. Design, test, and optimize prompts to guide LLM behavior effectively.
16. Implement few-shot, chain-of-thought, or function-calling techniques to enhance performance.
17. Orchestration & Automation
18. Use orchestration frameworks like LangChain, LlamaIndex, Semantic Kernel, or similar.
19. Create modular pipelines for composable AI development using open-source and enterprise tools.
20. Testing, Evaluation & Monitoring
21. Evaluate LLM-based systems using qualitative and quantitative metrics (e.g., accuracy, hallucination rate, latency).
22. Monitor performance in production and implement improvements based on user feedback or business outcomes.
23. Cloud Infrastructure & Deployment
24. Deploy AI systems on cloud platforms (e.g., AWS, Azure, GCP).
25. Use containerization and orchestration (Docker, Kubernetes) for scalable and resilient services.
26. Apply DevOps/MLOps practices for CI/CD, observability, and ongoing system reliability.
27. Documentation & Best Practices
28. Document AI workflows, data flows, and integration points.
29. Promote reusable components, prompt templates, and architecture patterns.
30. Share learnings, standards, and guidelines across the organization.

Skills:

1. Technical Skills:
2. Proven experience in full-stack development (front-end and back-end).
3. Hands-on expertise with AI solutioning, LLMs, RAG pipelines, and orchestration frameworks.
4. Proficiency with cloud platforms (AWS, Azure, or GCP) and containerized deployments.
5. Strong foundation in APIs, microservices, and system integrations.
6. Experience with vector databases (Pinecone, FAISS, Weaviate) and search/retrieval systems.
7. Proactive and strong interest in exploring new technology, especially in AI and emerging technologies.
8. Demonstrated ability to deliver projects related to AI (portfolio, GitHub, or production deployments).
9. Strong problem-solving, communication, and collaboration skills.
10. Soft Skills
11. Strong problem-solving and critical thinking.
12. Excellent communication and ability to translate technical solutions into business outcomes.
13. Self-driven and comfortable working in a fast-paced, evolving environment.
14. Understanding of key design principles.

Experience Requirements:

• Senior Developer: 4+ years, with at least 5 years in automation development and experience leading projects