Artificial Intelligence Engineer

Your Responsibilities:

• Full Stack Development: Design, develop, and maintain end-to-end AI solutions, including Front End interfaces, Back End services, and data pipelines.
• AI System Development: Implement and optimize AI models, ensuring they are scalable, maintainable, and production-ready. Work on GenAI, Agentic AI, and classic ML solutions.
• Enterprise Integration: Integrate AI solutions with existing enterprise systems and ensure seamless operation within the client's technology stack.
• Collaboration: Work closely with cross-functional teams, including data scientists, software engineers, and business stakeholders, to identify opportunities and deliver robust AI solutions.
• Technical Leadership: Provide technical guidance and mentorship to junior engineers. Lead the implementation of best practices in AI/ML development and deployment.
• Innovation: Stay updated with the latest advancements in AI/ML technologies and contribute to the development of innovative solutions.

Your Profile

Essential skills/knowledge/experience:

• Proficiency in Python and extensive experience with AI/ML/NLP libraries (eg, Hugging Face Transformers, spaCy, NLTK).
• Experience with state-of-the-art LLMs (eg, GPT series, Llama series, Mistral, Claude), including prompt engineering, fine-tuning, and evaluation.
• Hands-on experience with core GenAI frameworks (eg, LangChain, LlamaIndex) and Agentic AI frameworks (eg, AutoGen, CrewAI, LangGraph).
• Proficiency in MLOps/LLMOps tools (MLflow, Kubeflow, Docker, Kubernetes).
• Strong working knowledge and practical deployment experience on major cloud platforms (AWS, Azure, GCP), including their AI/ML services.
• Strong foundation in software engineering principles for building scalable, maintainable, and production-ready AI systems.
• Experience in designing and implementing enterprise-grade AI solutions, including RAG-based solutions with LLMs and vector databases (eg, Pinecone, Weaviate, FAISS).
• Proven experience in full stack development and AI/ML system implementation within enterprise environments.
• Strong grasp of advanced techniques such as complex task decomposition for agents, reasoning engines, knowledge graphs, and autonomous agent design.
• Excellent communication and stakeholder management skills.
• Experience working on client proposals and leading technical presentations.