Product Manager

Description

Think of TEKsystems Global Services (TGS) as the growth solution for enterprises today. We unleash growth through technology, strategy, design, execution and operations with a customer-first mindset for bold business leaders. We deliver cloud, data and customer experience solutions. Our partnerships with leading cloud, design and business intelligence platforms fuel our expertise. We value deep relationships, dedication to serving others and inclusion.

We drive positive outcomes for our people and our business, and we stay true to our commitments and act in harmony with our words. We exist to create significant opportunity for people to achieve fulfillment through career success.

Ready to join us? Heres what the opportunity supported through our TGS Talent Acquisition Team requires:

TEKsystems Global Services (TGS) is seeking a Product Manager to lead enterprise Data Operations initiatives and the delivery of multi-modal data annotation, prompt/context engineering, model evaluation and Agentic AI solutions. This role blends product ownership with product operations execution, enabling clients to operationalize their data for AI models and build AI agents while meeting enterprise standards for security, governance, scalability, and compliance.

You will define and drive product strategy while orchestrating endtoend delivery of Data Opsenabled AI platforms and agentic workflows that leverage AI frameworks.

Key Responsibilities

Product Management & Data Operations

Own the product vision, roadmap, and backlog for data and agentic AI capabilities across enterprise client engagements.

Translate business needs into outcome driven product/service requirements and architecture.

Define success metrics tied to data reliability/quality, AI agent effectiveness, automation value, and operational efficiency.

Develop PRDs, user stories, and acceptance criteria with a strong focus on operability, reuse, and scalability.

Lead cross functional delivery across diverse team of data engineering, ML engineering, platform, security, and client stakeholders.

Plan and execute complex programs spanning Data Ops, MLOps, and Agentic AI workflows using Agile delivery methods and AI tools.

Manage dependencies, risks, and timelines across multiple work streams and client environments.

Establish delivery governance including design reviews, readiness checks, operational handoffs, and postlaunch validation.

Drive implementation and modernization of enterprise Data Ops capabilities, including:

o Data ingestion, transformation, and orchestration

o Data modeling, metadata management, and lineage

o Data quality frameworks and observability

Partner with architects to ensure data tooling is production ready, secure, and well governed.

Define and track data SLAs, quality thresholds, and operational metrics in alignment with business use cases.

Agentic AI Solutions

Lead productization and delivery of Agentic AI systems for example:

o Agent orchestration frameworks (e.g., Lang Graph, AutoGenOSS, CrewAI, custom orchestration layers)

o Retrieval Augmented Generation (RAG) using open vector stores and search engines

o Toolusing agents that integrate with enterprise APIs, data platforms, and workflow systems

o Multiagent workflows for task decomposition, reasoning, validation, and escalation

Ensure implementations include:

o Prompt and workflow versioning

o Evaluation frameworks and benchmark testing

o Humanintheloop controls and fallback mechanisms

o Observability, logging, and traceability

Balance accuracy, latency, cost, reliability, and safety through iterative experimentation and data driven tuning.

Governance, Risk, and Compliance

Partner with security, legal, and compliance teams to ensure AI agents behave predictably and are in alignment.

Define controls for PII handling, model access, prompt data retention, and auditability.

Ensure solutions meet enterprise change management and incident response standards.

Stakeholder Leadership

Serve as a trusted partner to client product delivery leaders and sales executives.

Communicate tradeoffs, progress, risks, and value realization clearly and concisely.

Influence without authority across client and internal delivery ecosystems.

Required Qualifications

Bachelors degree in Computer Science, Information Systems, Data/Analytics, or equivalent experience.

710 years of experience across Product Management and / or Product Operations.

Proven experience delivering enterprise Data Ops platforms, quality data sets and/or AI/ML…