Job Description

Solves complex problems and help stakeholders make data- driven decisions by leveraging quantitative methods, such as machine learning. It often involves synthesizing large volume of information and extracting signals from data in a programmatic way.

Roles & Responsibilities

-----------------------------

Deep Learning

Architect and train

CNN/ViT models

for classification, detection, segmentation, and OCR. Build and optimize

RNN/LSTM/GRU models

for sequence learning, speech, or timeseries forecasting. Research and implement

transformer-based architectures

bridging vision and language tasks. Create scalable pipelines for data ingestion, annotation, augmentation, and synthetic data generation.

Agentic AI & Multi-Agent Frameworks

Design and implement multi-agent workflows using LangChain, LangGraph, CrewAI, or similar frameworks. Develop role hierarchies, state graphs, and integrations that enable

autonomous

vision + language workflows

. Optimize agent systems for

latency, cost, and reliability

.

LLM Fine-Tuning & Retrieval-Augmented Generation (RAG)

Fine-tune open-weight LLMs using LoRA/QLoRA, PEFT, or RLHF methods. Develop

RAG pipelines

integrating vector databases (FAISS, Weaviate, pgvector). Combine LLM reasoning with CNN/RNN perception modules in multimodal systems.

MLOps & Deployment at Scale

Develop reproducible training workflows with PyTorch/TensorFlow and experiment tracking (W&B, MLflow). Deploy models with TorchServe, Triton, or KServe on cloud AI stacks (AWS Sagemaker, GCP Vertex, Kubernetes). Optimize inference with ONNX/TensorRT, quantization, and pruning for

cloud and

edge devices

. Build robust APIs/micro-services (FastAPI, gRPC) and ensure CI/CD, monitoring, and automated retraining.

Desired Candidate

---------------------


B.S./M.S. in Computer Science, Electrical Engineering, Applied Math, or related discipline.

5+ years

building deep learning systems with

CNNs and RNNs in production

. Strong Python skills and Git workflows. Proven delivery of

computer vision pipelines (OCR, classification, detection)

. Hands-on experience with

LLM fine-tuning and multimodal AI

. Experience in

containerization (Docker)

and deployment on cloud AI platforms. Knowledge of

distributed training, GPU acceleration, and inference

optimization

.

Preferred Qualifications

Research experience in

transformer architectures (ViTs, hybrid CNN-RNNTransformer

models)

. Prior work in

sequence modeling for speech or time-series data

. Contributions to

open-source deep learning frameworks or vision/sequence

datasets

. Experience with

edge AI deployment

and hardware optimization

Competencies

----------------


Values: Integrity, Accountability, Inclusion, Innovation, Teamwork

ABOUT TE CONNECTIVITY

TE Connectivity plc (NYSE: TEL) is a global industrial technology leader creating a safer, sustainable, productive, and connected future. Our broad range of connectivity and sensor solutions enable the distribution of power, signal and data to advance next-generation transportation, energy networks, automated factories, data centers, medical technology and more. With more than 85,000 employees, including 9,000 engineers, working alongside customers in approximately 130 countries, TE ensures that EVERY CONNECTION COUNTS.

WHAT TE CONNECTIVITY OFFERS:

We are pleased to offer you an exciting total package that can also be flexibly adapted to changing life situations - the well-being of our employees is our top priority!


Competitive Salary Package Performance-Based Bonus Plans Health and Wellness Incentives Employee Stock Purchase Program Community Outreach Programs / Charity Events Employee Resource Group

IMPORTANT NOTICE REGARDING RECRUITMENT FRAUD

TE Connectivity has become aware of fraudulent recruitment activities being conducted by individuals or organizations falsely claiming to represent TE Connectivity. Please be advised that TE Connectivity

never requests payment or fees

from job applicants at any stage of the recruitment process. All legitimate job openings are posted exclusively on our official careers website at te.com/careers, and all email communications from our recruitment team will come

only from

actual

email addresses ending in @te.com

. If you receive any suspicious communications, we strongly advise you not to engage or provide any personal information, and to report the incident to your local authorities.


Across our global sites and business units, we put together packages of benefits that are either supported by TE itself or provided by external service providers. In principle, the benefits offered can vary from site to site.