AI / Gen AI Technical Lead with Azure - Only on w2

Title                                       : AI / GenAI Technical Lead with Azure

Location                               : Boston, MA (onsite)

Duration                              : 12+ Months

Rate                                       : $60/hr on w2

8 years of experience
The AI Tech Lead is responsible for architecting, designing, and leading end to end AI/GenAI solutions across enterprise programs. This role combines deep technical expertise with leadership to drive AI adoption, guide technical teams, collaborate with business stakeholders, and ensure scalable, secure, and compliant AI delivery. The AI Tech Lead acts as the primary technical authority for AI initiatives within the program/CoE.

Key Responsibilities
1. AI Solution Architecture & Design
•       Lead the architecture and design of AI/ML/GenAI solutions (LLMs, prompt engineering, vector search, RAG, NLP, computer vision, MLOps, automation).
•       Evaluate and select appropriate AI frameworks, models (open-source & proprietary), cloud services, and data pipelines.
•       Define scalable architectures using Azure, AWS, Google Cloud Platform, or hybrid environments.
•       Drive RAG pipelines, model fine tuning, and embeddings strategy for enterprise use cases.
2. Technical Leadership
•       Mentor and guide a team of AI engineers, data scientists, ML engineers, analysts, and automation engineers.
•       Conduct code reviews, design reviews, and ensure best practices in AI and software engineering.
•       Build reusable AI components, accelerators, and frameworks for the organization.
3. Delivery & Execution
•       Translate business requirements into technical specifications and AI solution approaches.
•       Lead POCs, pilots, and AI product lifecycle from ideation to deployment.
•       Ensure accuracy, performance, and reliability of AI systems through evaluations, testing, and optimizations.
•       Coordinate with DevOps/MLOps teams for CI/CD pipelines, model deployment, monitoring, and observability.
4. Data Engineering & Governance
•       Work closely with data engineering teams to define data ingestion, cleansing, feature engineering, and model training datasets.
•       Ensure compliance with enterprise data governance, privacy, security, and ethical AI guidelines.
•       Set up monitoring systems for model drift, data drift, and performance degradation.
5. Stakeholder Management
•       Collaborate with business leaders, product owners, architects, QA and Validation leads, and delivery partners.
•       Communicate technical concepts in a simple, business-friendly manner.
•       Prepare architecture documents, solution blueprints, and presentations for leadership reviews..