Lead end-to-end data and AI initiatives, architect AI solutions, build scalable data platforms, and mentor teams while ensuring compliance and governance.
We are looking for a Principal Technical Consultant – Data Engineering & AI who can lead modern data and AI initiatives end-to-end — from enterprise data strategy to scalable AI/ML solutions and emerging Agentic AI systems. This role demands deep expertise in cloud-native data architectures, advanced machine learning, and AI solution delivery, while also staying at the frontier of technologies like LLMs, RAG pipelines, and AI agents. You’ll work with C-level clients to translate AI opportunities into engineered outcomes.
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:
- Agentic AI & Autonomous Workflows (Emerging Focus):
- Governance, Security, and Responsible AI:- Establish best practices in data governance, access controls, metadata management, and auditability.
-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.
- 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.
- 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
Top Skills
Airflow
Autogen
Azure Event Hubs
Azure Ml
BigQuery
Ci/Cd
Cloud-Native Data Architectures
Crewai
Dagster
Dbt
Delta Lake
Docker
Iceberg
Kafka
Kinesis
Kubernetes
Langchain
Llms
Mlflow
Pinecone
Prefect
Sagemaker
Semantic Kernel
Snowflake
Spark
Terraform
Vertex Ai
Weaviate
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