Role Summary :

Design and develop production-ready AI/ML solutions using LLMs, Generative AI, NLP, deep learning, and cloud AI services. Act as a technical SME, collaborate across teams, and drive scalable AI solutions from design through deployment.

Key Responsibilities :

- Design, develop, and deploy scalable, production-ready AI/ML and GenAI solutions.

- Build and integrate LLM-based applications, NLP solutions, and cloud AI services.

- Collaborate with cross-functional teams to define solutions and drive key technical decisions.

- Troubleshoot complex AI/ML challenges and provide effective solutions across teams.

- Provide technical guidance, mentoring, and knowledge sharing to team members.

- Monitor solution performance, quality, and project progress to ensure successful delivery.

Must-Have Qualifications :

- 8+ years of overall professional experience, with substantial hands-on AI/ML engineering experience.

- Strong hands-on experience with Large Language Models (LLMs) and GenAI.

- Proficiency in Python and deep learning frameworks such as PyTorch or TensorFlow.

- Experience with NLP and cloud platforms such as AWS, Azure, or GCP.

- Experience building scalable, production-ready AI solutions.

- Strong technical leadership and problem-solving skills.

Nice to Have :

- Experience with RAG, embeddings, and vector databases.

- Knowledge of LLM fine-tuning, AI agents, or LLMOps/MLOps.

- Experience with Docker, Kubernetes, and CI/CD.

- Exposure to multimodal AI, computer vision, or image processing.

Behavioral Competencies :

- Strong problem-solving and analytical skills.

- Effective communication and collaboration.

- Technical leadership and decision-making.

- Ownership and accountability.

- Mentoring and knowledge sharing.

Why Join Us :

- Work on enterprise-scale AI and Generative AI initiatives.

- Solve complex business problems using emerging LLM and cloud AI technologies.

- Work alongside multidisciplinary engineering and technology teams.

- Influence AI solution architecture and production engineering practices.

- Build expertise across LLMs, GenAI, cloud, NLP, deep learning, and AI engineering.

- Contribute to a culture of continuous learning, innovation, collaboration, and knowledge sharing.