Senior Technical Consultant, Data and AI

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