Offer summary

Qualifications:

Master's or PhD in Computer Science, Data Science, or AI/ML., Over 12 years of experience in data platforms and AI/ML systems., Proficiency in cloud-native design on Azure, AWS, or GCP., Expertise in Python, SQL, Spark, and AI frameworks like PyTorch or TensorFlow..

Key responsibilities:

Design and implement AI/ML systems including predictive modeling and NLP.

Lead the development of scalable data platforms and pipelines.

Explore and develop agentic AI solutions with LLMs and autonomous workflows.

Advise clients and lead cross-functional teams in AI and data initiatives.

Job description

We are looking for a Principal Technical Consultant – Data Engineering & AI who can lead modern data and AI initiatives endtoend — from enterprise data strategy to scalable AIML solutions and emerging Agentic AI systems. This role demands deep expertise in cloudnative 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 Clevel clients to translate AI opportunities into engineered outcomes.

Roles and Responsibilities
AI Solution Architecture & Delivery:
Design and implement productiongrade AIML systems, including predictive modeling, NLP, computer vision, and timeseries forecasting.
Architect and operationalize endtoend ML pipelines using MLflow, SageMaker, Vertex AI, or Azure ML — covering feature engineering, training, monitoring, and CICD.
Deliver retrievalaugmented generation (RAG) solutions combining LLMs with structured and unstructured data for highcontext 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, CICD pipelines
Implement observability and reliability features into data pipelines and ML systems.

Agentic AI & Autonomous Workflows (Emerging Focus):
Explore and implement LLMpowered agents using frameworks like LangChain, Semantic Kernel, AutoGen, or CrewAI.
Develop prototypes of taskoriented AI agents capable of planning, tool use, and interagent collaboration for domains such as operations, customer service, or analytics automation.
Integrate agents with enterprise tools, vector databases (e.g., Pinecone, Weaviate), and functioncalling APIs to enable contextrich 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, crossfunctional 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, AIML systems, and enterprise solutioning
Cloudnative design experience on Azure, AWS, or GCP
Expert in Python, SQL, Spark, ML frameworks (scikitlearn, PyTorch, TensorFlow)
Deep understanding of MLOps, orchestration, and cloud AI tooling
Handson 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 AIML
Certifications: Azure AI102, AWS ML Specialty, GCP ML Engineer, or equivalent
Exposure to agentic architectures, LLM finetuning, or multiagent collaboration frameworks
Experience with opensource contributions, conference talks, or whitepapers in AIData