Key Responsibilities:
Provide technical leadership and architectural direction in the design and implementation of advanced Generative AI applications.
Oversee LLM fine-tuning, optimization, and evaluation strategies to address complex business needs across domains.
Lead the development and orchestration of modular AI workflows using frameworks like LangChain, LlamaIndex, AutoGen, CrewAI, and integrate them into enterprise-grade systems.
Architect and manage scalable, production-ready AI deployments on cloud platforms (AWS, Azure, GCP), ensuring cost-effectiveness, high availability, and performance.
Drive the adoption and execution of MLOps and LLMOps best practices, establishing CI/CD pipelines for reliable and automated model deployment and monitoring.
Oversee end-to-end ML pipeline development using Apache Airflow, MLflow, and DBT, ensuring robustness, scalability, and data integrity.
Guide the implementation and continual improvement of NER models and other deep learning solutions using TensorFlow and PyTorch.
Strategically explore and lead the integration of multimodal LLMs and OCR technologies for real-world applications.
Champion the design and development of robust APIs using FastAPI for scalable model serving.
Act as a key liaison between technical teams and business stakeholders, translating business needs into AI-driven solutions.
Mentor and upskill a team of ML/AI engineers, promoting a culture of innovation, collaboration, and continuous learning.
Stay at the forefront of AI/ML research, evaluate emerging technologies, and guide their adoption within the organization to maintain technical leadership.
Required Skills:
Proven expertise in Python, Natural Language Processing, and LLM ecosystem.
Deep experience with agentic frameworks (LangChain, AutoGen, CrewAI), fine-tuning LLMs, and building custom distilled models.
Strong background in ML pipeline architecture, data orchestration, and cloud-native ML workflows.
Hands-on experience with TensorFlow and PyTorch, particularly for NER and other NLP tasks.
Working knowledge of multimodal models, OCR systems, and their integration into AI applications.
Experience in architecting FastAPI-based APIs for high-throughput ML services.
Familiarity with container orchestration using Docker and Kubernetes in a cloud environment.
Track record of implementing MLOps/CI-CD best practices and production-grade ML systems.
Demonstrated leadership in delivering AI solutions at scale, managing cross-functional teams, and influencing technical direction.
Excellent problem-solving, decision-making, and communication skills.
Strong mentorship mindset and the ability to grow and inspire high-performing teams.
