Job Description
Key Responsibilities
- Independently design and implement end-to-end AI, Generative AI, and agentic AI solutions, taking full technical ownership of architecture, development, deployment, and optimization.
- Architect and build frameworks for autonomous AI agents capable of planning, reasoning, and executing multi-step tasks using APIs, tools, enterprise systems, and external services.
- Develop robust integration patterns for LLMs, ML models, and agentic systems with enterprise applications, including databases, APIs, and legacy platforms, enabling intelligent tool-using agents.
- Hands-on model selection, fine-tuning, evaluation, and optimization for LLMs, transformer-based architectures, diffusion models, and other advanced AI models.
- Design advanced system components such as agent memory, reflection loops, vector stores, and long-horizon planning mechanisms to support scalable agentic intelligence.
- Work closely with cross-functional stakeholders to identify automation and augmentation opportunities, translating business needs into AI architecture and actionable solution designs.
- Implement strong governance, compliance, and Responsible AI controls, ensuring transparency, security, and safe deployment of all autonomous and generative systems.
- Define and operationalize monitoring, observability, and performance evaluation frameworks for continuous improvement of AI models and agentic systems.
- Drive cost optimization strategies across AI infrastructure, training pipelines, inference workloads, and cloud resource utilization.
- Ensure measurable value realization by validating that implemented AI solutions deliver tangible business impact and align with organizational objectives.
- Collaborate with the AI/ML engineering community and provide technical direction when needed, while maintaining personal hands-on ownership of core development activities.
Education and Qualification
- Master's degree (preferred) or Bachelor's degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, or a related technical field.
- Advanced certifications or specialization in AI/ML architecture, cloud platforms, or generative AI technologies are a plus
- Certification in Databricks, Azure AI Engineer or Azure Data Scientist Associate
- 8-10 years of experience in AI/ML solution design and architecture, including at least 4+ years in Generative AI and agentic AI systems.
- Proven track record in architecting large-scale AI platforms, integrating LLMs, and designing multi-agent systems.
- Strong proficiency in Python and deep understanding of ML frameworks (e.g., PyTorch, TensorFlow, LangChain, Hugging Face Transformers).
- Expertise in LLMs (e.g., GPT, Claude, LLaMA), vector databases, and prompt engineering strategies.
- Hands-on experience with agentic frameworks (e.g., LangChain Agents, AutoGPT, OpenAgents, CrewAI) and orchestration of autonomous agents.
- Deep knowledge of planning, reasoning, and decision-making architectures for autonomous systems.
- Experience in cloud-native AI architecture on Azure, including Azure ML/AI platform, Copilot Studio, and Azure Foundry.
- Strong background in containerization and orchestration (Docker, Kubernetes) for scalable AI deployments.
- Familiarity with reinforcement learning, symbolic reasoning, and neuro-symbolic AI approaches.
- Experience with real-time data processing, event-driven architectures, and MLOps best practices for production-grade AI systems.
