Responsibilities: 

Architecture Design: 

• Design and implement scalable, secure, and high-performance architectures for Generative AI applications. 

• Integrate Generative AI models into existing platforms, ensuring compatibility and performance optimization. 

Model Development and Deployment: 

• Fine-tune pre-trained generative models for domain-specific use cases. 

• Data Collection, Sanitization and Data Preparation strategy for Model fine tuning. 

• Evaluate, select, and deploy appropriate Generative AI frameworks (e.g., PyTorch, TensorFlow, Crew AI, Autogen, Langraph, Agentic code, Agentflow). 

Innovation and Strategy: 

• Stay up to date with the latest advancements in Generative AI and recommend innovative applications to solve complex business problems. 

• Define and execute the AI strategy roadmap, identifying key opportunities for AI transformation. 

Collaboration and Leadership: 

• Collaborate with cross-functional teams, including data scientists, engineers, and business stakeholders. 

• Mentor and guide team members on AI/ML best practices and architectural decisions. 

• Should be able to lead a team of data scientists, GenAI engineers, devops and Software Developer. 

Performance Optimization: 

• Monitor the performance of deployed AI models and systems, ensuring robustness and accuracy. 

• Optimize computational costs and infrastructure utilization for large-scale deployments. 

Ethical and Responsible AI: 

• Ensure compliance with ethical AI practices, data privacy regulations, and governance frameworks. 

• Implement safeguards to mitigate bias, misuse, and unintended consequences of Generative AI. 

Required Skills: 

• Advanced programming skills in Python and fluency in data processing frameworks like Apache Spark. 

• Should have strong knowledge on LLM’s foundational model (OpenAI GPT4o, O1, Claude, Gemini etc), while need to have strong knowledge on opensource Model’s like Llama 3.2, Phi etc. 

• Proven track record with event-driven architectures and real-time data processing systems. 

• Familiarity with Azure DevOps and other LLMOps tools for operationalizing AI workflows. 

• Deep experience with Azure OpenAI Service and vector DBs, including API integrations, prompt engineering, and model fine-tuning. Or equivalent tech in AWS/GCP. 

• Knowledge of containerization technologies such as Kubernetes and Docker. 

• Comprehensive understanding of data lakes and strategies for data management. 

• Expertise in LLM frameworks including Langchain, Llama Index, and Semantic Kernel. 

• Proficiency in cloud computing platforms such as Azure or AWS. 

• Exceptional leadership, problem-solving, and analytical abilities. 

• Superior communication and collaboration skills, with experience managing high-performing teams. 

• Ability to operate effectively in a dynamic, fast-paced environment.