AI Engineer

About the Role

We are looking for a visionary AI/ML Engineer to join our team and drive the next wave of intelligent automation. In this role, you won't just build models; you will architect real-world solutions using state-of-the-art Generative AI, LLMs, and RAG pipelines. You will sit at the intersection of complex data science and business strategy, turning unstructured data into intelligent, actionable insights.

Key Responsibilities

• Model Innovation: Design, develop, and deploy robust AI/ML models tailored for real-world business impact.
• Generative AI Leadership: Lead the development of Generative AI solutions, including LLM implementation, NLP, and AI-driven automation.
• End-to-End Pipelines: Build and maintain scalable AI pipelines and successfully integrate models into high-performance production environments.
• Strategic Collaboration: Act as a technical bridge, collaborating with cross-functional teams to solve complex business challenges using AI.
• Optimization: Fine-tune and optimize machine learning models to ensure maximum performance, accuracy, and cost-efficiency.
• Data Intelligence: Work with diverse, structured, and unstructured datasets to extract and visualize intelligent insights.
• Future-Proofing: Stay at the forefront of the industry by researching and adopting emerging AI trends and technologies.

Required Skills and Qualifications

• Core Tech: Strong professional experience in Python and standard AI/ML frameworks.
• GenAI Stack: Hands-on experience with Generative AI, LLMs, LangChain, OpenAI APIs, or similar orchestration tools.
• Deep Learning & NLP: Solid foundation in Machine Learning, Deep Learning, and Natural Language Processing.
• Libraries: Proficiency with TensorFlow, PyTorch, Scikit-learn, or Hugging Face.
• Next-Gen AI: Familiarity with Vector Databases (Pinecone/Milvus), RAG pipelines, and the development of autonomous AI agents is a significant plus.
• Cloud & Infrastructure: A solid understanding of APIs, databases, and cloud ecosystems (AWS, Azure, or GCP).
• Stakeholder Articulation: The ability to explain complex AI concepts to non-technical stakeholders in a clear, persuasive manner.
• Active Listening: A collaborative mindset that involves listening to business pain points and translating them into technical requirements.
• Documentation Excellence: Strong writing skills for creating clear technical documentation, API guides, and project reports.
• Proactive Problem Solving: A self-starter attitude with the ability to navigate ambiguity and communicate roadblocks early.