Roles and Responsibilities
- AI Solution Architecture & Delivery:
- - Design and implement production-grade AI/ML systems, including predictive modeling, NLP, computer vision, and time-series forecasting.
- - Architect and operationalize end-to-end ML pipelines using MLflow, SageMaker, Vertex AI, or Azure ML — covering feature engineering, training, monitoring, and CI/CD.
- - Deliver retrieval-augmented generation (RAG) solutions combining LLMs with structured and unstructured data for high-context enterprise use cases.
- Data Platform & Engineering Leadership: -Build scalable data platforms with modern lakehouse patterns using: - Ingestion: Kafka, Azure Event Hubs, Kinesis - Storage & Processing: Delta Lake, Iceberg, Snowflake, BigQuery, Spark, dbt- Workflow Orchestration: Airflow, Dagster, Prefect- Infrastructure: Terraform, Kubernetes, Docker, CI/CD pipelines - Implement observability and reliability features into data pipelines and ML systems.
- Agentic AI & Autonomous Workflows (Emerging Focus): - Explore and implement LLM-powered agents using frameworks like LangChain, Semantic Kernel, AutoGen, or CrewAI. - Develop prototypes of task-oriented AI agents capable of planning, tool use, and inter-agent collaboration for domains such as operations, customer service, or analytics automation.- Integrate agents with enterprise tools, vector databases (e.g., Pinecone, Weaviate), and function-calling APIs to enable context-rich decision making.
- Governance, Security, and Responsible AI:- Establish best practices in data governance, access controls, metadata management, and auditability. - Ensure compliance with security and regulatory requirements (GDPR, HIPAA, SOC2).- Champion Responsible AI principles including fairness, transparency, and safety. Consulting, Leadership & Practice Growth:- Lead large, cross-functional delivery teams (10–30+ FTEs) across data, ML, and platform domains.- Serve as a trusted advisor to clients’ senior stakeholders (CDOs, CTOs, Heads of AI). - Mentor internal teams and contribute to the development of accelerators, reusable components, and thought leadership.
Key Skills
- 12+ years of experience across data platforms, AI/ML systems, and enterprise solutioning
- Cloud-native design experience on Azure, AWS, or GCP
- Expert in Python, SQL, Spark, ML frameworks (scikit-learn, PyTorch, TensorFlow)
- Deep understanding of MLOps, orchestration, and cloud AI tooling
- Hands-on with LLMs, vector DBs, RAG pipelines, and foundational GenAI principles
- Strong consulting acumen: client engagement, technical storytelling, stakeholder alignment
Qualifications
- Master’s or PhD in Computer Science, Data Science, or AI/ML
- Certifications: Azure AI-102, AWS ML Specialty, GCP ML Engineer, or equivalent
- Exposure to agentic architectures, LLM fine-tuning, or multi-agent collaboration frameworks
- Experience with open-source contributions, conference talks, or whitepapers in AI/Data
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