SR. AI Developer

TE Connectivity

4 - 9 years

10 - 14 Lacs

bengaluru

Posted:1 day ago| Platform:

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Skills Required

python cnn deep learning tensorflow git natural language processing scikit-learn neural networks numpy machine learning artificial intelligence docker pandas django data science computer vision pytorch keras flask ocr

Work Mode

Work from Office

Job Type

Full Time

Job Description

Job Overview

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

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