AI Engineer

Job description

The Role

As an Artificial Intelligence Engineer , you will bring deep expertise in full-stack development and AI system design to complex enterprise environments. This role demands hands-on experience in building and deploying AI/ML solutions, a strong grasp of software engineering principles, and proficiency with cloud platforms. You will play a key role in developing scalable, intelligent systems that drive innovation and deliver measurable business value.

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 (e.g., Hugging Face Transformers, spaCy, NLTK). Experience with state-of-the-art LLMs (e.g., GPT series, Llama series, Mistral, Claude), including prompt engineering, fine-tuning, and evaluation. Hands-on experience with core GenAI frameworks (e.g., LangChain, LlamaIndex) and Agentic AI frameworks (e.g., 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 (e.g., 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. </p