sr. ai developer

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.

• 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.

• 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

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 , and all email communications from our recruitment team will come only from actual email addresses ending in 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.