Find Jobs
Find Jobs Near You – Available Work in Your Location
Skip to job details
TC
Tata Consultancy Services Limited
SME Network Autonomy
Career Insights for Generative Artificial Intelligence Engineer
See where this job fits in the broader career landscape. Knowing your career path helps you see what's possible from here.
Scorecard
Based on New Jersey data
Review key factors to help you decide if this role fits your goals. How is this calculated?
What they do
A Generative Artificial Intelligence Engineer develops, designs, and manages generative models and algorithms that support the generation of new content in the form of images, text, audio, and other multimedia. They utilize GPTs, GANs, VAEs, and other deep learning architectures to craft systems capable of generating data. May work with data scientists, machine learning engineers, and software developers.
$127,942 / year median in New Jersey
Job Description
Must Have Technical/Functional Skills Networking Expertise
- Deep protocol level knowledge of BGP, OSPF, MPLS, VXLAN, and SD WAN
- Experience operating and troubleshooting carrier grade or hyperscale networks AI/ML & GenAI
- Hands on experience applying AI/ML models to networking use cases (e.g., time series forecasting, anomaly detection)
- Familiarity with ML frameworks such as TensorFlow or PyTorch Software & Automation
- Expert proficiency in Python (preferred), Go, or C++
- Strong experience with CI/CD pipelines and DevOps practices
- Extensive use of Ansible and Terraform for network automation Cloud Native Networking
- Experience managing network functions in Kubernetes environments (CNI plugins)
- Hands on exposure to public cloud networking across AWS, Azure, and GCP Roles & Responsibilities The Senior Network Autonomy Lead will define and execute the vision for next‑generation autonomous networks.
Generic Managerial Skills:
Strategic Vision & Roadmap- Define and own a multi‑year roadmap for network autonomy, spanning Autonomy Levels 0 through to Level 5
- Drive the organizational transition from Infrastructure‑as‑Code (IaC) to Intent‑Based Networking (IBN)
- Evaluate, pilot, and integrate advanced technologies such as Digital Twins for network modeling, simulation, and optimization
- Establish KPIs and success metrics for autonomy maturity, resiliency, and operational efficiency 2. Technical & Architecture Leadership
- Architect and oversee AI/ML‑driven platforms for predictive maintenance, anomaly detection, and root‑cause analysis
- Lead the design and implementation of closed‑loop automation systems where networks observe, decide, and act without human intervention
- Standardize and govern telemetry, APIs, and data models (YANG, JSON, gNMI) across multi‑vendor domains
- Ensure scalability, resilience, and security of autonomous control loops in production environments 3. Cross‑Functional Leadership
- Lead and mentor senior engineers, architects, and data scientists
- Partner with security, cloud, and platform teams to embed autonomy across the infrastructure stack
- Influence product, procurement, and vendor ecosystems to align with the autonomy vision