Info Way Solutions
Job Title: Senior Manager - Generative AI
Location:
Dallas, TX Department:
AI & Data Science Employment Type:
Full-time
Job Summary We are looking for a Senior Manager - Generative AI to drive implementation and delivery of GenAI-based solutions across business domains. You will bring a strong foundation in cloud-native technologies, AI/ML systems, and emerging GenAI methodologies including LLMs, RAG, and agentic architectures. This role requires hands-on leadership in engineering execution, technical client interactions, and cross-team collaboration.
Key Responsibilities GenAI Technical Execution
Lead the implementation of GenAI use cases by orchestrating the development of
multi-modal ingestion pipelines ,
LLM integrations , and
retrieval-augmented generation (RAG)
architectures. Build and manage containerized services, including
REST APIs , using serverless platforms or Kubernetes as appropriate. Implement AI solutions on cloud platforms (Azure, AWS, GCP), leveraging services like
vector stores ,
unstructured DBs , and
in-memory datastores . Apply hands-on experience with
agent orchestration libraries
such as
LangGraph ,
CrewAI , or
AutoGen
to deliver modular, reusable GenAI solutions. Project Delivery s Collaboration
Work closely with technical leads, data scientists, and product managers to convert GenAI concepts into scalable and maintainable solutions. Own the technical delivery of projects - plan sprints, coordinate with engineering teams, and ensure timely delivery with high quality. Provide engineering leadership across multiple initiatives, balancing short-term priorities with long-term architectural goals. Stakeholder s Client Engagement
Support the translation of business problems into GenAI execution strategies. Collaborate with client-side technical teams to ground expectations, address risks, and ensure solution feasibility. Contribute to solution design discussions, architecture reviews, and deployment planning. Communication s Planning
Create and present technical deliverables such as
architecture diagrams ,
execution playbooks , and
solution walk-throughs . Help develop reusable collateral (presentations, documentation) to support pre- sales, solutioning, and internal alignment. Support program and project management workflows using tools like
JIRA ,
Azure DevOps , etc.
Required Skills s Experience Technical Expertise
10-12 years of experience in building AI/ML or data systems with at least 2-3 years in cloud-native environments. Experience building, containerizing, and deploying APIs using tools like FastAPI, Flask, or Node.js. Familiarity with LLM integration strategies, RAG frameworks, and building pipelines using embeddings and vector DBs. Working knowledge of agentic GenAI using frameworks like LangGraph, CrewAI, or AutoGen. Solid grasp of cloud technologies: serverless compute, storage solutions, unstructured/NoSQL databases, and network architecture. Delivery s Leadership
Proven track record of managing end-to-end technical delivery of AI or software solutions. Ability to lead mid-sized technical teams and coordinate across cross-functional groups. Experience working with client stakeholders to align on requirements, timelines, and solution feasibility. Soft Skills
Strong verbal and written communication; ability to articulate technical concepts to both technical and non-technical audiences. Good organizational skills and comfort working with project management tools (e.g., JIRA, Azure DevOps). Strong team player with a problem-solving mindset and the ability to work in a fast- paced, agile environment.
Dallas, TX Department:
AI & Data Science Employment Type:
Full-time
Job Summary We are looking for a Senior Manager - Generative AI to drive implementation and delivery of GenAI-based solutions across business domains. You will bring a strong foundation in cloud-native technologies, AI/ML systems, and emerging GenAI methodologies including LLMs, RAG, and agentic architectures. This role requires hands-on leadership in engineering execution, technical client interactions, and cross-team collaboration.
Key Responsibilities GenAI Technical Execution
Lead the implementation of GenAI use cases by orchestrating the development of
multi-modal ingestion pipelines ,
LLM integrations , and
retrieval-augmented generation (RAG)
architectures. Build and manage containerized services, including
REST APIs , using serverless platforms or Kubernetes as appropriate. Implement AI solutions on cloud platforms (Azure, AWS, GCP), leveraging services like
vector stores ,
unstructured DBs , and
in-memory datastores . Apply hands-on experience with
agent orchestration libraries
such as
LangGraph ,
CrewAI , or
AutoGen
to deliver modular, reusable GenAI solutions. Project Delivery s Collaboration
Work closely with technical leads, data scientists, and product managers to convert GenAI concepts into scalable and maintainable solutions. Own the technical delivery of projects - plan sprints, coordinate with engineering teams, and ensure timely delivery with high quality. Provide engineering leadership across multiple initiatives, balancing short-term priorities with long-term architectural goals. Stakeholder s Client Engagement
Support the translation of business problems into GenAI execution strategies. Collaborate with client-side technical teams to ground expectations, address risks, and ensure solution feasibility. Contribute to solution design discussions, architecture reviews, and deployment planning. Communication s Planning
Create and present technical deliverables such as
architecture diagrams ,
execution playbooks , and
solution walk-throughs . Help develop reusable collateral (presentations, documentation) to support pre- sales, solutioning, and internal alignment. Support program and project management workflows using tools like
JIRA ,
Azure DevOps , etc.
Required Skills s Experience Technical Expertise
10-12 years of experience in building AI/ML or data systems with at least 2-3 years in cloud-native environments. Experience building, containerizing, and deploying APIs using tools like FastAPI, Flask, or Node.js. Familiarity with LLM integration strategies, RAG frameworks, and building pipelines using embeddings and vector DBs. Working knowledge of agentic GenAI using frameworks like LangGraph, CrewAI, or AutoGen. Solid grasp of cloud technologies: serverless compute, storage solutions, unstructured/NoSQL databases, and network architecture. Delivery s Leadership
Proven track record of managing end-to-end technical delivery of AI or software solutions. Ability to lead mid-sized technical teams and coordinate across cross-functional groups. Experience working with client stakeholders to align on requirements, timelines, and solution feasibility. Soft Skills
Strong verbal and written communication; ability to articulate technical concepts to both technical and non-technical audiences. Good organizational skills and comfort working with project management tools (e.g., JIRA, Azure DevOps). Strong team player with a problem-solving mindset and the ability to work in a fast- paced, agile environment.